Building an Agent Aware Telemetry Pipeline
Why agent awareness matters now
Agent awareness matters now because the environment that AI and automation operate in has shifted in three major ways:
There is rising complexity in systems and context
- Modern software stacks span multi-cloud, microservices, edge, and AI components, making it harder to track what’s happening and why.
- Agents that are aware of context—not just executing prompts—can navigate these distributed environments and respond intelligently to changes.
- Without awareness, agents act in silos, missing dependencies, upstream/downstream effects, or compliance risks.
AI is an active participants not just a tool
- In the early days, AI models were passive: you fed a prompt, got an output.
- Today, agentic AI systems make decisions, call tools, and chain tasks together—meaning they affect production workflows, user experience, and cost.
- Awareness gives them the ability to:
- Validate their own actions against golden signals or policies.
- Recognize when they lack information and query the right source.
- Adapt across multiple models, frameworks, or data pipelines.
Prompt engineering is shifting to context engineering
- Static prompts can’t handle noisy, dynamic, log-heavy environments.
- Context-ready telemetry—structured logs, metrics, traces—provides agents with situational awareness: what’s normal, what’s broken, what matters.
- This makes AI systems more reliable, repeatable, and cost-effective.
- In other words, awareness reduces hallucinations, false positives, and wasted cycles.
Organizations need faster incident response, smarter automation, and lower observability costs. Additionally, organizations are moving from experiments to production AI agents, where blind spots can cause outages or compliance failures. Also, teams that give agents awareness through telemetry pipelines, governance, and integrations are able to scale safely and unlock new efficiencies.
Agent awareness turns AI from “smart autocomplete” into a trustworthy co-pilot for real-world systems. Without it, organizations risk noisy, fragile, or even unsafe automation. With it, they gain reliability, efficiency, and confidence.
From store first to act in real time
For decades, observability and automation followed a store-first pattern:
- Data is collected and stored in massive log warehouses or monitoring platforms.
- Engineers query and analyze after the fact.
- Insights arrive minutes, hours, or even days later.
This model worked when systems were slower, with fewer moving parts, AI and automation were not yet part of the critical path, and storage costs were lower than compute or real-time processing.
But the lag between events, awareness and action left teams blind during incidents, and AI agents without fresh context.
Today’s modern environments demand immediacy:
- Microservices and multi-cloud generate data at massive volume and velocity.
- AI agents need live telemetry to make safe, reliable decisions.
- Business impact is measured in seconds of downtime or delayed response.
Instead of “store everything and figure it out later,” organizations now need to:
- Filter, enrich, and normalize telemetry at ingest (so agents see only what matters).
- Apply governance and redaction in-flight to prevent exposure of sensitive fields.
- Route enriched events in real-time to the right systems, whether that’s an incident responder, AI agent, or workflow engine.
This enables streaming awareness, where agents act on data as it flows—not after it’s warehoused.
Agent awareness is what unlocks this act-in-real-time capability:
- Context-Ready Telemetry: Agents don’t just see raw logs; they see structured signals aligned to golden metrics.
- Adaptive Decisioning: Agents detect anomalies, validate against baselines, and choose actions without waiting for humans to parse stored data.
- Cost Efficiency: By shaping data before storage, organizations reduce log rehydration and storage overhead, while giving agents the actionable slice they need.
Agent awareness is critical today because the world has moved from a store-first mindset to an act-in-real-time reality. Without awareness, agents are reactive and brittle. With awareness, they become proactive, cost-efficient, and trustworthy partners in operations.
What changes when AI agents are in the loop
Today’s AI agents aren’t just analyzing after the fact, they’re in the loop of operations, decisions, and workflows. Before agents, data pipelines stored everything first, and engineers or dashboards made sense of it later. With agents in the loop, actions are taken in real time — opening tickets, scaling clusters, routing alerts, even interacting with customers.
Now decision making is continuous with human-in-the-loop and agent-in-the-loop. When AI agents are in the loop, awareness shifts from “nice-to-have context for later analysis” to the critical substrate that makes automation safe, accurate, and cost-effective. Without it, agents are unpredictable. With it, they become dependable partners in operations and user experience.
The cost, noise, and latency trap
The cost trap starts with a store-first mindset, meaning capturing everything before shaping it. With AI agents in the loop, costs can explode. The noise trap refers to raw telemetry that is high-cardinal, repetitive, and noisy. AI agents without awareness chase false correlations, generate junk tickets, or miss true anomalies. The latency trap is a nod to traditional observability which tolerated a minutes-to-hours lag. But AI agents operate in the loop — they need sub-second, real-time signals to route incidents, scale services, or triage users. Without awareness, agents either act too late or act blindly.
Agent awareness breaks the cycle by moving from:
- Store-first → act later → to Shape-first → act now.
- Blind ingestion → to governed, enriched, validated context.
- Unbounded growth in cost/noise/latency → to controlled, real-time efficiency.
Without awareness, AI agents magnify the cost, noise, and latency trap that already plagues observability. With awareness, they flip it: real-time action, lower spend, and higher confidence.
Design principles for an agent aware pipeline
Active Engagement for developer self-serve
Give builders a clear, safe path to wire telemetry to agents.
- Productized onboarding: golden templates for common services (web API, job runner, LLM gateway) with sane defaults (sampling, PII redaction, trace links).
- Shift-left previews: sandbox every change (filters, routes, redactions) on a sample stream; show volume deltas and cost impact before merge.
- Policy-as-code + catalog: versioned routing/enrichment policies stored with the service repo; surfaced in a UI catalog for discovery and reuse.
- Guardrails by default: denylist + allowlist patterns for secrets/PII; schema contracts and validation tests on CI.
- Feedback to the author: per-route SLOs, dropped-event reasons, and “what your agent saw” traces.
Quick wins (what to ship)
- A CLI/SDK + UI wizard that outputs a policy stub (filters, routes, enrichers) and opens a PR in the service repo.
- A change simulator: paste logs/traces → see what will be routed, redacted, or enriched, plus estimated storage/egress deltas.
KPIs
- Time-to-first-signal (TTFS) for new service
- % changes shipped with preview
- Policy reuse rate; policy drift rate
Active Routing with intent and context
Stop firehosing. Route by meaning, not by topic.
- Intent detection at ingest: classify events (e.g., incident_candidate, security_event, usage_signal, cost_regression) using rules + light models.
- Context-aware fan-out: combine intent with resource (service, tenant, region), sensitivity (PII/PHI), and urgencyto decide destinations: incident bot, SIEM, analytics lake, LLM memory, on-call pager.
- Priority queues + backpressure: urgent paths have dedicated lanes; apply adaptive sampling to non-critical lanes under load.
- Fail-modes are explicit: define fail-open/closed per route (e.g., security → fail-closed; analytics → fail-open with buffering).
- Policy composability: small, testable routing blocks (match → transform → deliver) assembled per team.
Example routing rule (illustrative YAML)
when:
all:
- attr.service == "checkout"
- attr.level in ["error","fatal"]
- metric.http_5xx_rate_1m > 0.05
then:
enrich: ["trace_link", "owner_team", "runbook_url"]
redact: ["card.number", "user.email"]
deliver:
- to: "incident_agent"
priority: "p1"
format: "incident.v1"
- to: "observability_store"
retention_days: 14
else:
sample: { rate: 0.1 }
deliver: [{ to: "analytics_lake", batch: "1m" }]
KPIs
- P50/P95 ingest→action latency (per route)
- % events routed to “noisy” sinks (should trend down)
- Agent-initiated false positive/negative rate
Active Analysis for in-stream enrichment
Add the missing context before data hits an agent.
- Normalization & parsing: unify timestamp/level/service keys; parse JSON-in-text; attach span/trace IDs.
- Entity & topology joins: attach owner_team, service_tier, deploy_sha, feature_flag, tenant_id, and blast-radius via CMDB/topology graph.
- Derived signals: compute rolling rates, error budgets, SLO burn, deduped fingerprints, and log-to-metricconversions at the edge.
- Lightweight anomaly checks: windowed z-scores/seasonal baselines to flag candidate incidents without a heavy model dependency.
- Semantic labels for agents: compact, model-friendly fields (e.g., intent="payment_failure", risk="customer_impacting").
Example enrichment (illustrative)
enrich:
- add_trace_link: true
- join:
from: "topology_graph"
keys: ["service","region"]
fields: ["owner_team","service_tier","runbook_url"]
- derive:
http_5xx_rate_1m: rate(count(status>=500), window="1m")
slo_burn_5m: burn(error_budget, window="5m")
- fingerprint:
on: ["message_template","code","endpoint"]
ttl: "10m"
- set_intent:
rule: if http_5xx_rate_1m>0.05 then "incident_candidate"
KPIs
- % events enriched with owner/runbook
- Mean prompts/tokens per agent action (should drop as enrichment improves)
- Time from anomaly to mitigative action
Cross-cutting concerns (don’t skip these)
- Governance & privacy: redaction patterns unit-tested; PII detection recall metrics; per-route data residency controls.
- Observability of the pipeline: traces around every policy step; route-level dashboards; dead-letter stream with replay.
- Versioning & rollout: blue/green policy deploys, canaries by team/tenant, automatic rollback on SLO breach.
- Performance budgets: cap per-stage CPU/latency; watchdogs for enrichment timeouts; queue pressure signals.
- Cost controls: pre-storage shaping, dynamic sampling, downsampling/of-interest retention tiers; “cost diff” in PRs.
- Human loopbacks: agent actions must post rationale + evidence; easy “was this helpful?” feedback into training signals.
Minimal viable implementation (starter kit)
- Collectors: OpenTelemetry Collector / Fluent Bit with processors for parse, redact, sample, route.
- Policy engine: declarative rules (YAML/JSON) evaluated at ingest; CI tests + traffic replay.
- Context joins: lightweight sidecar or edge cache pulling from topology/flags; TTLs + fallbacks.
- Agent interface: a clean schema (incident.v1, remediation.v1) + zero-copy trace links.
- Preview & simulate: shadow the new policy on a sampled mirror stream; diff volume/cost/actions before enable.
What “good” looks like (outcomes)
- 50–80% reduction in agent prompt/context volume with higher action accuracy.
- Sub-second ingest→agent latency on P1 routes, sustained under load.
- 30–60% lower storage/rehydration spend via shape-before-store.
- Observable, reversible, and auditable policy changes tied to owner teams.
Reference architecture at a glance
┌────────────────────────────────────────────────────────────────────────────┐
│ SOURCES (Apps/Infra/AI) │
│ Apps • APIs • Jobs • Gateways • LLM endpoints • DBs • K8s • Cloud logs │
└───────────────┬───────────────────────────────────────────────┬────────────┘
│ │
(collect/forward) (side signals)
│ │
┌───────▼────────┐ ┌────────▼────────┐
│ Ingress Edge │ OTLP/HTTP, syslog, │ Context Stores │
│ (Collectors) │ file tail, webhooks │ Topology/CMDB │
└───────┬────────┘ │ Feature flags │
│ │ Deploy metadata │
parse • normalize • redact • drop • sample │ Tenants/ACLs │
│ └────────┬─────────┘
│ (low-latency join/cache)
┌───────▼──────────────────────────────────────────────────────────┐
│ Policy & Enrichment Engine │
│ intent detect → route → enrich(join) → derive(metrics/fp) → out │
│ (declarative rules; versioned; preview/simulate on live sample) │
└───────┬───────────────────────────────┬───────────────────────────┘
│ P1 “hot lane” │ P2/P3 lanes (adaptive)
│ │
┌────────▼─────────┐ ┌──────▼─────────┐
│ Priority Queue(s)│ │ Batch Buffer │
│ backpressure │ │ (micro-batch) │
└────────┬─────────┘ └──────┬─────────┘
│ │
┌─────────────▼────────────┐ ┌──────────▼───────────┐
│ ACTION DESTINATIONS │ │ STORAGE DESTINATIONS │
│ Incident/Remediation │ │ Observability store │
│ Agent (schema:incident) │ │ Data lake/warehouse │
│ Chat/SOC bots, SOAR │ │ SIEM/data retention │
│ Ticketing/Runbooks │ │ Long-term archive │
└─────────────┬────────────┘ └──────────┬────────────┘
│ │
(audit trail & rationale from agents; replay & rehydrate)
──────────────────────────────────────────────────────────────────────────────
CONTROL PLANE (for humans & agents)
Catalog • Policy repo (Git) • Preview/simulation • Approvals • Secrets
Governance (PII, residency) • Schema registry • Cost guardrails • SLOs
Pipeline observability (traces/metrics/logs) • Canary/blue-green deploys
Core building blocks
Data Plane
- Ingress Edge: OpenTelemetry Collector / Fluent Bit tiers; uniform parse/normalize/redact/sampling.
- Policy & Enrichment Engine: Declarative rules (YAML/JSON) for intent → route → enrich → derive. Supports shadow/preview and traffic replay.
- Context Joins (low-latency): Owner/team, service tier, deploy SHA, feature flags, tenant, region, runbook URL, blast-radius from topology.
- Priority Lanes: P1 hot lane (sub-second budget) vs P2/P3 with adaptive sampling/backpressure.
- Destinations:
- Action: incident/remediation agents, SOAR, ticketing, chatbots.
- Storage: observability store (short retention), SIEM, lake/warehouse, archive.
Control Plane
- Catalog & Self-serve: Templates per service; one-click policy stubs; sandbox preview with volume/cost/actiondiffs.
- Governance: Redaction unit tests; PII classifiers; data residency/ACLs; schema registry; per-route fail-open/closed.
- Ops of the Pipeline: Traces around every stage; dead-letter with replay; blue/green policies; canary by service/tenant; cost budgets.
Minimal viable stack (starter choices)
- Collectors: OTEL Collector + processors (transform, attributes, redaction), Fluent Bit at edges.
- Policy Engine: OTEL routing/transform + custom rule-eval or a stream processor (Flink/Kafka Streams) for hot lanes.
- Transport: Kafka/NATS/PubSub for queues; S3/GCS for batch sinks.
- Context Cache: Redis/KeyDB/Cloud cache fronting topology/flags/CMDB.
- Schema: Protobuf/JSON Schema for incident.v1, remediation.v1, telemetry_enriched.v1.
- Destinations: Incident agent (LLM+tools), ticketing (Jira/ServiceNow), observability store (ClickHouse/Quickwit/Elastic), SIEM, lake.
Latency and reliability budgets (typical)
- P1 (user-impacting): Ingest→agent ≤ 750 ms p95; ≥ 99.9% delivery; fail-closed on governance.
- P2 (ops): 1–5 s; ≥ 99.5% delivery; graceful degrade with adaptive sampling.
- Batch/analytics: Minutes; cost-optimized paths.
Example rule sketch (illustrative)
when:
all:
- attr.service == "payments"
- derived.http_5xx_rate_1m > 0.05
then:
enrich:
- trace_link: true
- join: [owner_team, service_tier, runbook_url, deploy_sha, tenant_id]
- derive: [slo_burn_5m, fingerprint(message_template, endpoint)]
- label: { intent: "incident_candidate", risk: "customer_impacting" }
redact: ["pii.email", "card.number"]
deliver:
- to: incident_agent # hot lane
schema: incident.v1
priority: p1
- to: observability_store
retention_days: 14
else:
sample: { rate: 0.1 }
deliver: [{ to: analytics_lake, batch: "1m" }]
What to monitor (SLOs and KPIs)
- Latency: Ingest→decision (per route, p50/p95).
- Quality: Agent FP/FN rate, action success, “helpful?” feedback.
- Coverage: % enriched with owner/runbook/trace link; intent classification recall/precision.
- Cost: GB/day pre vs post-shaping; rehydration frequency; hot-lane token/prompt size.
- Reliability: Queue depth, DLQ rate, policy rollback events.
Deployment patterns
- Blue/green policy versions with automatic rollback on SLO breach.
- Shadow preview on sampled live stream to show diffs before enabling.
- Multi-region edges with local governance (residency) + central control plane.
- Backpressure playbook: shed non-critical lanes first; preserve P1.
Anti-patterns (avoid)
- Firehose to agents (no shaping).
- Global sampling toggles (use per-intent/per-tenant budgets).
- Opaque enrichment (no provenance/rationale).
- Store-then-classify for P1 signals (classify at ingest).
Ingest layers for logs, metrics, traces, events
Here’s a practical, “use-it-today” map of ingest layers for an agent-aware telemetry pipeline, covering logs, metrics, traces, and events. It’s organized by layers (left-to-right in data flow) and then shows per–signal-type specifics you’ll want to implement.
0) Source and Transport
Goal: get signals in reliably with minimal coupling and preserve provenance.
- Receivers/Protocols:
- Logs: OTLP/HTTP or gRPC, syslog, file tail, Fluent Bit forward, Cloud vendor sinks, app stdout.
- Metrics: OTLP, Prometheus scrape/remote_write, StatsD.
- Traces: OTLP, Jaeger/Zipkin shims.
- Events: OTLP (custom signals), webhooks (GitOps, deploys, feature flags), cloud audit, product analytics bus.
- Provenance headers: x-telemetry-source, x-region, build SHA, service version, tenant/org.
- Clock sanity: NTP drift alerts; if drift > threshold, apply safe timestamp policy (don’t reorder blindly).
1) Edge Collect and Pre-Filter
Goal: cut obvious noise early and protect hot lanes from floods.
- Stateless drops: health checks, debug in prod, verbose retries, known “heartbeat” patterns.
- Adaptive guards: per-tenant/per-service rate caps; burst absorption buffers.
- PII “first look”: lightweight regex/keyword redaction before anything else (defense-in-depth).
2) Parse and Normalize
Goal: a consistent, model-friendly envelope for agents and rules.
- Canonical keys: ts, service, env, region, tenant_id, trace_id, span_id, level/severity, code, endpoint, message.
- Logs: robust parsers (JSON, key-value, multiline/stack traces) → structured records.
- Metrics: enforce naming, units, and label constraints; coerce bad label types; de-snakecase if needed.
- Traces: ensure W3C traceparent propagation; repair missing service/resource attributes.
- Events: map diverse payloads (deploy, feature flag, user action, security) into event.v1 schema.
3) Governance and Safety (in-stream)
Goal: agents never see what they shouldn’t and storage stays compliant.
- Redaction & tokenization: emails, card PANs, auth tokens, IPs; deterministic tokens for joinability.
- Residency & ACLs: route by jurisdiction/tenant; fail-closed on policy mismatch.
- Schema validation: reject or quarantine records that violate *.v1 schemas; emit DLQ with reason.
4) Correlate and Attribute
Goal: add missing context that agents need to decide.
- Topology joins: owner team, service tier, runbook URL, dependencies, blast radius.
- Deploy context: build SHA, image tag, feature flags, canary cohort.
- User/Tenant context: plan, SLA tier, entitlement flags.
- Trace links: attach trace_id/span_id to logs & events; backfill when possible.
5) Derive and Shape (Active Analysis)
Goal: create signal from raw and keep tokens small for agents.
- Derived metrics: error rate windows, saturation, SLO burn, anomaly flags (z-score/seasonal).
- Fingerprinting & dedupe: templatize messages, compute stable hashes to suppress repeats.
- Sampling & compaction: dynamic rates by intent/tenant/route; convert repetitive logs → metrics (log-to-metric).
6) Intent and Priority (Active Routing)
Goal: route by meaning (not topic) with explicit urgency.
- Intent classification: rules + lightweight models → incident_candidate, security_event, cost_regression, usage_signal, etc.
- Priority lanes: P1 hot lane (sub-second budget), P2 ops (seconds), P3 batch (minutes).
- Fail-mode policy: security/intents → fail-closed; analytics → fail-open with buffering.
7) Queueing and Delivery
Goal: isolate backpressure and guarantee the right QoS.
- Queues: dedicated topics per intent/priority; backpressure signals to upstream samplers.
- Destinations:
- Action: incident/remediation agents (schemas incident.v1, remediation.v1), chat/SOAR, ticketing.
- Stores: observability store (short retention), SIEM, lake/warehouse, cold archive.
- Audit trail: every agent action carries rationale, evidence, input pointers.
Per-Signal-Type Notes (what’s special & what to watch)
Logs
- Risks: cardinality explosions (dynamic IDs in labels), multiline parsing, PII.
- Do: parse to structure early; compute fingerprints; move repetitive counts to log-derived metrics; attach trace_idwhen present.
- Don’t: route raw high-volume debug to agents.
- Hot-lane trigger examples: surge in error fingerprint + deploy SHA change + customer-impacting endpoint.
Metrics
- Risks: high-card labels; unit drift; scrape gaps.
- Do: enforce label budgets; normalize units; precompute SLO burn/error budgets; map to resource (service, workload, region).
- Don’t: send raw high-freq time series to agents; send summaries/aggregates + links instead.
- Hot-lane trigger examples: http_5xx_rate_1m > X, latency_p95 > SLO, burn-rate > threshold.
Traces
- Risks: missing propagation; oversized span attributes; runaway event volume.
- Do: guarantee W3C propagation; cap attributes/events; extract span events → intent (e.g., db_deadlock, rate_limit).
- Don’t: forward entire traces to agents by default; include span path + key attrs and a trace link.
- Hot-lane trigger examples: error spans on tier-1 services with blast-radius > N tenants.
Events (Business, Security, Product, Platform)
- Risks: heterogeneous payloads; ungoverned PII; clock skew.
- Do: normalize to event.v1 (type, subject, source, ts, actor, object, severity, tenant_id); apply the same redaction rules as logs.
- Don’t: treat all events as equal—most are context for agents, a few are triggers.
- Hot-lane trigger examples: “deploy started/ended” near incident, feature-flag flip correlated with error fingerprint.
Canonical Envelope (suggested)
Keep it small, consistent, and model-friendly.
{
"ts": "2025-10-08T16:12:03.412Z",
"kind": "log|metric|trace|event",
"intent": "incident_candidate|security_event|usage_signal|cost_regression|none",
"priority": "p1|p2|p3",
"service": "checkout",
"env": "prod",
"region": "us-east-1",
"tenant_id": "acme",
"trace_id": "…",
"span_id": "…",
"attributes": { "endpoint": "/pay", "code": "ERR42", "slo_burn_5m": 2.3 },
"provenance": { "source": "otelcol", "node": "edge-us1", "version": "1.2.3" }
}
Illustrative OTEL Collector Config (single node, multi-signal)
This shows where layers land; adapt to your stack.
Receivers:
otlp: { protocols: { http: {}, grpc: {} } }
filelog:
include: [ /var/log/app/*.log ]
operators: # Edge parse & pre-filter
- type: regex_parser
regex: '^(?P<ts>[^ ]+) (?P<level>[^ ]+) (?P<message>.*)$'
- type: add
field: resource.service.name
value: checkout
prometheus:
config:
scrape_configs: [ { job_name: "k8s", static_configs: [ { targets: ["app:9090"] } ] } ]
processors:
filter/drop-noise:
logs:
include:
match_type: strict
resource_attributes:
- key: env
value: prod
exclude:
bodies: [ "healthcheck OK" ]
attributes/redact:
actions:
- key: user.email
action: delete
- key: card.number
action: delete
attributes/normalize:
actions:
- key: env
action: upsert
value: prod
transform/derive:
error_mode: ignore
log_statements:
- context: log
statements:
- set(attributes.fingerprint, sha256(body))
- set(attributes.intent, if(attributes.level == "error", "incident_candidate", "none"))
metric_statements:
- context: datapoint
statements:
- set(attributes.service, resource.attributes["service.name"])
routing/intent:
attribute_source: attribute
from_attribute: attributes.intent
table:
incident_candidate: [ p1_hot_lane ]
none: [ default_lane ]
exporters:
kafka/p1_hot_lane: { brokers: [ "kafka:9092" ], topic: "telemetry.p1" }
kafka/default_lane: { brokers: [ "kafka:9092" ], topic: "telemetry.p2" }
clickhouse/store: { endpoint: "http://clickhouse:8123" }
service:
pipelines:
logs:
receivers: [ filelog, otlp ]
processors: [ filter/drop-noise, attributes/redact, attributes/normalize, transform/derive, routing/intent ]
exporters: [ kafka/p1_hot_lane, kafka/default_lane, clickhouse/store ]
metrics:
receivers: [ prometheus, otlp ]
processors: [ attributes/normalize, transform/derive ]
exporters: [ kafka/default_lane, clickhouse/store ]
traces:
receivers: [ otlp ]
processors: [ attributes/normalize ]
exporters: [ kafka/p1_hot_lane, clickhouse/store ]Quality Gates and SLOs (what to watch)
- Latency (per lane): ingest→decision p95 (P1 ≤ 750 ms; P2 ≤ 5 s).
- Coverage: % records with owner/runbook/trace link; % events with valid schema.
- Governance: PII redaction recall/precision; residency violations (should be 0).
- Cost: GB/day pre vs post-shaping; token size per agent action; rehydration frequency.
- Accuracy: intent precision/recall; agent FP/FN and remediation success.
Quick Starter Checklist
- Define canonical envelope & schemas (log.v1, metric.v1, trace.v1, event.v1, incident.v1).
- Stand up edge collectors with pre-filter + basic redaction.
- Implement normalize → govern → correlate → derive → intent → route as discrete steps.
- Carve P1 hot lane with strict budgets and fail-closed governance.
- Add preview/shadow mode and cost diffs for every policy change.
Real time processors for enrichment and redaction
What follows is a practical, “use-it-today” map of ingest layers for an agent-aware telemetry pipeline. It’s organized by layers (left-to-right in data flow) and then shows per–signal-type specifics you’ll want to implement.
0) Source and Transport
Goal: get signals in reliably with minimal coupling and preserve provenance.
- Receivers/Protocols:
- Logs: OTLP/HTTP or gRPC, syslog, file tail, Fluent Bit forward, Cloud vendor sinks, app stdout.
- Metrics: OTLP, Prometheus scrape/remote_write, StatsD.
- Traces: OTLP, Jaeger/Zipkin shims.
- Events: OTLP (custom signals), webhooks (GitOps, deploys, feature flags), cloud audit, product analytics bus.
- Provenance headers: x-telemetry-source, x-region, build SHA, service version, tenant/org.
- Clock sanity: NTP drift alerts; if drift > threshold, apply safe timestamp policy (don’t reorder blindly).
1) Edge Collect and Pre-Filter
Goal: cut obvious noise early and protect hot lanes from floods.
- Stateless drops: health checks, debug in prod, verbose retries, known “heartbeat” patterns.
- Adaptive guards: per-tenant/per-service rate caps; burst absorption buffers.
- PII “first look”: lightweight regex/keyword redaction before anything else (defense-in-depth).
2) Parse and Normalize
Goal: a consistent, model-friendly envelope for agents and rules.
- Canonical keys: ts, service, env, region, tenant_id, trace_id, span_id, level/severity, code, endpoint, message.
- Logs: robust parsers (JSON, key-value, multiline/stack traces) → structured records.
- Metrics: enforce naming, units, and label constraints; coerce bad label types; de-snakecase if needed.
- Traces: ensure W3C traceparent propagation; repair missing service/resource attributes.
- Events: map diverse payloads (deploy, feature flag, user action, security) into event.v1 schema.
3) Governance and Safety (in-stream)
Goal: agents never see what they shouldn’t and storage stays compliant.
- Redaction & tokenization: emails, card PANs, auth tokens, IPs; deterministic tokens for joinability.
- Residency & ACLs: route by jurisdiction/tenant; fail-closed on policy mismatch.
- Schema validation: reject or quarantine records that violate *.v1 schemas; emit DLQ with reason.
4) Correlate and Attribute
Goal: add missing context that agents need to decide.
- Topology joins: owner team, service tier, runbook URL, dependencies, blast radius.
- Deploy context: build SHA, image tag, feature flags, canary cohort.
- User/Tenant context: plan, SLA tier, entitlement flags.
- Trace links: attach trace_id/span_id to logs & events; backfill when possible.
5) Derive and Shape (Active Analysis)
Goal: create signal from raw and keep tokens small for agents.
- Derived metrics: error rate windows, saturation, SLO burn, anomaly flags (z-score/seasonal).
- Fingerprinting & dedupe: templatize messages, compute stable hashes to suppress repeats.
- Sampling & compaction: dynamic rates by intent/tenant/route; convert repetitive logs → metrics (log-to-metric).
6) Intent and Priority (Active Routing)
Goal: route by meaning (not topic) with explicit urgency.
- Intent classification: rules + lightweight models → incident_candidate, security_event, cost_regression, usage_signal, etc.
- Priority lanes: P1 hot lane (sub-second budget), P2 ops (seconds), P3 batch (minutes).
- Fail-mode policy: security/intents → fail-closed; analytics → fail-open with buffering.
7) Queueing and Delivery
Goal: isolate backpressure and guarantee the right QoS.
- Queues: dedicated topics per intent/priority; backpressure signals to upstream samplers.
- Destinations:
- Action: incident/remediation agents (schemas incident.v1, remediation.v1), chat/SOAR, ticketing.
- Stores: observability store (short retention), SIEM, lake/warehouse, cold archive.
- Audit trail: every agent action carries rationale, evidence, input pointers.
Per-Signal-Type Notes (what’s special and what to watch)
Logs
- Risks: cardinality explosions (dynamic IDs in labels), multiline parsing, PII.
- Do: parse to structure early; compute fingerprints; move repetitive counts to log-derived metrics; attach trace_idwhen present.
- Don’t: route raw high-volume debug to agents.
- Hot-lane trigger examples: surge in error fingerprint + deploy SHA change + customer-impacting endpoint.
Metrics
- Risks: high-card labels; unit drift; scrape gaps.
- Do: enforce label budgets; normalize units; precompute SLO burn/error budgets; map to resource (service, workload, region).
- Don’t: send raw high-freq time series to agents; send summaries/aggregates + links instead.
- Hot-lane trigger examples: http_5xx_rate_1m > X, latency_p95 > SLO, burn-rate > threshold.
Traces
- Risks: missing propagation; oversized span attributes; runaway event volume.
- Do: guarantee W3C propagation; cap attributes/events; extract span events → intent (e.g., db_deadlock, rate_limit).
- Don’t: forward entire traces to agents by default; include span path + key attrs and a trace link.
- Hot-lane trigger examples: error spans on tier-1 services with blast-radius > N tenants.
Events (Business, Security, Product, Platform)
- Risks: heterogeneous payloads; ungoverned PII; clock skew.
- Do: normalize to event.v1 (type, subject, source, ts, actor, object, severity, tenant_id); apply the same redaction rules as logs.
- Don’t: treat all events as equal—most are context for agents, a few are triggers.
- Hot-lane trigger examples: “deploy started/ended” near incident, feature-flag flip correlated with error fingerprint.
Canonical Envelope (suggested)
Keep it small, consistent, and model-friendly.
{
"ts": "2025-10-08T16:12:03.412Z",
"kind": "log|metric|trace|event",
"intent": "incident_candidate|security_event|usage_signal|cost_regression|none",
"priority": "p1|p2|p3",
"service": "checkout",
"env": "prod",
"region": "us-east-1",
"tenant_id": "acme",
"trace_id": "…",
"span_id": "…",
"attributes": { "endpoint": "/pay", "code": "ERR42", "slo_burn_5m": 2.3 },
"provenance": { "source": "otelcol", "node": "edge-us1", "version": "1.2.3" }
}
Illustrative OTEL Collector Config (single node, multi-signal)
This shows where layers land; adapt to your stack.
receivers:
otlp: { protocols: { http: {}, grpc: {} } }
filelog:
include: [ /var/log/app/*.log ]
operators: # Edge parse & pre-filter
- type: regex_parser
regex: '^(?P<ts>[^ ]+) (?P<level>[^ ]+) (?P<message>.*)$'
- type: add
field: resource.service.name
value: checkout
prometheus:
config:
scrape_configs: [ { job_name: "k8s", static_configs: [ { targets: ["app:9090"] } ] } ]
processors:
filter/drop-noise:
logs:
include:
match_type: strict
resource_attributes:
- key: env
value: prod
exclude:
bodies: [ "healthcheck OK" ]
attributes/redact:
actions:
- key: user.email
action: delete
- key: card.number
action: delete
attributes/normalize:
actions:
- key: env
action: upsert
value: prod
transform/derive:
error_mode: ignore
log_statements:
- context: log
statements:
- set(attributes.fingerprint, sha256(body))
- set(attributes.intent, if(attributes.level == "error", "incident_candidate", "none"))
metric_statements:
- context: datapoint
statements:
- set(attributes.service, resource.attributes["service.name"])
routing/intent:
attribute_source: attribute
from_attribute: attributes.intent
table:
incident_candidate: [ p1_hot_lane ]
none: [ default_lane ]
exporters:
kafka/p1_hot_lane: { brokers: [ "kafka:9092" ], topic: "telemetry.p1" }
kafka/default_lane: { brokers: [ "kafka:9092" ], topic: "telemetry.p2" }
clickhouse/store: { endpoint: "http://clickhouse:8123" }
service:
pipelines:
logs:
receivers: [ filelog, otlp ]
processors: [ filter/drop-noise, attributes/redact, attributes/normalize, transform/derive, routing/intent ]
exporters: [ kafka/p1_hot_lane, kafka/default_lane, clickhouse/store ]
metrics:
receivers: [ prometheus, otlp ]
processors: [ attributes/normalize, transform/derive ]
exporters: [ kafka/default_lane, clickhouse/store ]
traces:
receivers: [ otlp ]
processors: [ attributes/normalize ]
exporters: [ kafka/p1_hot_lane, clickhouse/store ]Quality Gates and SLOs (what to watch)
- Latency (per lane): ingest→decision p95 (P1 ≤ 750 ms; P2 ≤ 5 s).
- Coverage: % records with owner/runbook/trace link; % events with valid schema.
- Governance: PII redaction recall/precision; residency violations (should be 0).
- Cost: GB/day pre vs post-shaping; token size per agent action; rehydration frequency.
- Accuracy: intent precision/recall; agent FP/FN and remediation success.
Quick Starter Checklist
- Define canonical envelope & schemas (log.v1, metric.v1, trace.v1, event.v1, incident.v1).
- Stand up edge collectors with pre-filter + basic redaction.
- Implement normalize → govern → correlate → derive → intent → route as discrete steps.
- Carve P1 hot lane with strict budgets and fail-closed governance.
- Add preview/shadow mode and cost diffs for every policy change.
Here’s a practitioner playbook for real-time enrichment and redaction processors in an agent-aware telemetry pipeline. It’s opinionated, latency-safe, and ready to drop into OTEL Collector / Fluent Bit / stream processors (Kafka Streams, Flink, Beam).
Goals (why these processors exist)
- Give agents context, not sludge: attach owners, runbooks, deploy info, trace links, SLO burn, fingerprints.
- Never leak sensitive data: deterministic tokenization before any agent or sink sees the record.
- Hit hot-lane budgets: p95 sub-second end-to-end on P1 routes, even under burst.
Processing Order (hot path)
parse → normalize → redaction(core) → enrichment(light) → derive → intent → routing → redaction(defense-in-depth)
- Redaction(core) happens before enrichment so sensitive fields never join outward.
- Redaction(defense-in-depth) runs again right before egress (catch regressions).
Enrichment Processors (real-time safe)
1) Canonical Normalize
Unify keys/types for model-friendly inputs.
- What: ts, service, env, region, tenant_id, trace_id, span_id, severity, code, endpoint.
- Notes: coerce timestamps to ISO8601, lowercase enums, trim label cardinality.
OTEL transform (snippet)
processors:
transform/normalize:
log_statements:
- set(resource.attributes["service"], resource.attributes["service.name"])
- set(attributes.env, coalesce(resource.attributes["deployment.environment"], "prod"))
- set(attributes.region, resource.attributes["cloud.region"])
2) Trace Linking (logs/events ⇄ traces)
- What: attach trace_id/span_id to logs/events; recover from headers (traceparent) or MDC.
- Why: unlock cross-signal correlation and agent drill-downs.
OTEL transform
processors:
transform/trace-link:
log_statements:
- set(attributes.trace_id, coalesce(attributes.trace_id, body.matches("trace_id=(\\w+)")[1]))
- set(attributes.span_id, coalesce(attributes.span_id, attributes["otel.span_id"]))
3) Topology/Ownership Join (low-latency cache)
- What: enrich with owner_team, service_tier, runbook_url, blast_radius.
- How: sidecar/cache (Redis/KeyDB) seeded from CMDB; TTL 60–300s; fallbacks if miss.
Pseudo (Flink/KStreams)
enriched = incoming
.leftJoin(topologyCache, Keys.serviceRegion(),
(rec, topo) -> rec.put("owner_team", nn(topo.team))
.put("service_tier", nn(topo.tier))
.put("runbook_url", nn(topo.runbook)));
4) Deploy/Feature Context
- What: deploy_sha, image_tag, feature_flags, canary_cohort.
- Use: correlate spikes with releases; let agents propose rollbacks.
OTEL transform
processors:
transform/deploy:
log_statements:
- set(attributes.deploy_sha, resource.attributes["git.sha"])
- set(attributes.feature_flags, resource.attributes["feature.flags"])
5) Derived Signals (cheap math, windowed)
- What: rolling error rates, SLO burn, anomaly flags (z-score/seasonality if cached), log-to-metric compaction.
- Why: shrink tokens, boost precision for agent triggers.
OTEL transform (illustrative)
processors:
transform/derive:
metric_statements:
- context: datapoint
statements:
- set(attributes.slo_burn_5m, attributes.error_rate_5m * 5.0)
log_statements:
- set(attributes.fingerprint, sha256(template(body)))
6) Semantic Labels (agent-ready)
- What: intent, risk, customer_impacting, blast_radius.
- How: rule-first, model-assisted; must be explainable and unit-tested.
Rule sketch
when:
all:
- attr.service_tier == "tier1"
- attr.http_5xx_rate_1m > 0.05
then:
set: { intent: "incident_candidate", risk: "customer_impacting" }
Redaction Processors (deterministic, layered)
A) Pattern Redaction (fast path)
- Targets: emails, tokens, PANs, IPv4/IPv6, SSNs, UUIDs, cookies.
- Action: remove or tokenize deterministically (HMAC w/ rotating key).
OTEL attributes
processors:
attributes/redact:
actions:
- key: user.email
action: delete
- key: auth.token
action: delete
Deterministic tokenization (stream UDF, pseudo)
def tok(value: str, salt: bytes) -> str:
return "tok_" + hmac_sha256(salt, value)[:16] # stable, non-reversibleB) Structure-Aware Redaction
- JSON paths: $.card.number, $.user.ssn, $.headers.Authorization.
- Strategy: delete or replace with token; keep last 4 if safe (•••• 1234).
Fluent Bit (examples)
[FILTER]
Name modify
Match *
Remove_key user.email
Remove_key headers.authorizationC) Model-Assisted PII (optional, shadowed)
- Scope: free-text log bodies only; run in shadow first; strict budgets.
- Fail-closed: if classifier times out, drop body or route to quarantine.
Performance and Reliability Budgets
- P1 hot lane: total processing ≤ 150–250 ms budget at edge; joins must be cache-hit ≥ 99%.
- Timeouts: each enrichment ≤ 20–30 ms; redact always on CPU only; no network calls on hot path except cache.
- Fallbacks: on cache miss → set owner_team="unknown", add needs_context=true, keep routing safe.
- Backpressure: expose queue depth; drop/shed non-critical enrichers first (derived metrics before ownership, never redact).
Idempotency and Provenance
- Attach enriched_by with processor versions and hashes:
"provenance": {
"processors": ["normalize@1.4.2","redact@2.1.0","topology@0.9.8"],
"policy_version":"2025-10-08.3"
}
- Use fingerprint as de-dupe key; never re-tokenize an already tokenized field (tok_* guard).
Testing and Rollout
- Unit tests: regex redaction recall/precision on gold corpora; schema validation.
- Replay tests: run processors on a sampled historical stream, compare volume/cost/action diffs.
- Shadow mode: emit enriched+redacted to a shadow topic; gate by SLOs before promotion.
- Canaries: per service/tenant; auto-rollback on latency/governance breach.
Concrete Config Examples
OTEL Collector (logs) — hot lane
receivers:
otlp: { protocols: { http: {}, grpc: {} } }
processors:
transform/normalize: {}
attributes/redact:
actions:
- key: user.email
action: delete
- key: card.number
action: delete
transform/trace-link: {}
transform/deploy: {}
transform/derive:
log_statements:
- set(attributes.fingerprint, sha256(body))
routing/intent:
attribute_source: attribute
from_attribute: attributes.intent
table: { incident_candidate: [ p1 ], none: [ p2 ] }
exporters:
kafka/p1: { brokers: ["kafka:9092"], topic: "telemetry.p1" }
kafka/p2: { brokers: ["kafka:9092"], topic: "telemetry.p2" }
service:
pipelines:
logs:
receivers: [ otlp ]
processors: [ transform/normalize, attributes/redact, transform/trace-link, transform/deploy, transform/derive, routing/intent ]
exporters: [ kafka/p1, kafka/p2 ]
Fluent Bit (edge) — regex redact + normalize
[FILTER]
Name modify
Match *
Remove_key user.email
Remove_key headers.authorization
[FILTER]
Name record_modifier
Match *
Record env prod
Kafka Streams (Java) — deterministic token + join
KStream<Key, Rec> s = builder.stream("logs.raw");
KStream<Key, Rec> redacted = s.mapValues(rec -> {
rec.put("user_id_tok", tok(rec.get("user_id")));
rec.remove("user_id");
return rec;
});
KStream<Key, Rec> enriched = redacted.leftJoin(ownerCache, keyByServiceRegion(),
(rec, own) -> rec.put("owner_team", nn(own.team)).put("runbook_url", nn(own.runbook)));
enriched.to("logs.enriched");
Observability of the Processors
- Emit processor metrics: redact_hits, redact_misses, join_latency_ms, cache_hit_ratio, derive_latency_ms, intent_precision/recall.
- Trace each stage (processor.name, policy_version, record.fingerprint) for step-by-step forensics.
KPIs that prove it’s working
- Governance: 0 residency violations; ≥ 99.5% redaction recall on known PII patterns.
- Latency: P1 ingest→agent ≤ 750 ms p95; per-processor ≤ 30 ms p95.
- Efficiency: −30–60% storage vs. raw; −40–80% agent token size with higher action accuracy.
- Coverage: ≥ 95% records with owner_team and runbook_url; ≥ 98% logs linked to traces where traces exist.
- Quality: drop in agent FP/FN; faster MTTR on P1s.
Handy “starter” redaction patterns (safe defaults)
- Email: (?i)\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,}\b
- PAN (Luhn pre-check): \b(?:\d[ -]*?){13,19}\b
- Bearer token header: (?i)authorization:\s*bearer\s+[a-z0-9._-]+
- IPv4: \b(?:(?:25[0-5]|2[0-4]\d|[01]?\d?\d)(?:\.|$)){4}\b
Smart sinks for storage, search, analytics, and AI agents
Get a concise, battle-tested blueprint for smart sinks in an agent-aware telemetry stack so every destination (storage, search, analytics, AI) receives the right data, in the right shape, with explicit intent and governance.
Sinks are smart if they’re intent-aware, schema-strict, policy-driven, action-ready and cost-controlled.
Storage Sinks (hot, warm, cold)
Purpose: durable record of truth with cost controls.
- Hot store (7–30d): fast ingest + recent queries; short retention.
Typical: ClickHouse / Loki / Quickwit (hot index).- Ingest schema: telemetry_enriched.v1
- Index: ts, intent, service, tenant, fingerprint, trace_id
- Controls: per-tenant quotas; TTL; row TTL by intent; small column codecs.
- Warm store (30–180d): cheaper, slower scans.
Typical: S3/GCS + columnar (Parquet) with partitioning (dt=/intent=/service=). - Cold archive (≥180d): compliance only.
Typical: Glacier/Deep Archive with manifest for selective rehydration.
KPIs: storage $/GB by tier, rehydration frequency, % queries served from hot vs warm.
Search Sinks (low-latency investigations)
Purpose: ad-hoc hunts, root cause, developer self-serve.
- Engines: Quickwit/Elasticsearch/OpenSearch (logs), ClickHouse (mixed), Jaeger/Tempo (traces).
- Shaping for search:
- Pre-tokenized fields: fingerprint, message_template
- PII removed; owner_team, deploy_sha, runbook_url present
- Trace cross-links (trace_id, span_path)
- Query rails: saved views per intent; query timeouts; sampled previews before heavy scans.
KPIs: p95 query latency, hit ratio of saved views, scan bytes/query.
Analytics Sinks (batch & BI/ML)
Purpose: trends, cost, product usage, training features.
- Warehouses/Lakes: BigQuery/Snowflake/Databricks + Iceberg/Delta/Apache Hudi.
- Contracts: analytics_telemetry.v1 (denormalized) + slowly changing dims for owners/topology.
- Compaction: log→metric rollups (1m/5m), fingerprint counts, percentile sketches.
- Governance: data contracts in CI; column-level lineage; residency tags.
KPIs: freshness SLA, cost per successful dashboard, % rollups vs raw scans.
AI Agent Sinks (action lanes)
Purpose: give agents compact, verifiable inputs with guardrails.
- Schemas:
- incident.v1 (symptoms, scope, severity, links to evidence)
- remediation.v1 (candidate action, pre-checks, blast radius, rollback)
- memory.v1 (summarized, PII-safe episodic memory with TTL)
- Payload rules: max token size; include evidence handles (trace/log links) not raw blobs.
- Safety: fail-closed on PII/residency; rationale & evidence required for every agent action; human override path.
KPIs: agent action success rate, token/decision, FP/FN rate, time ingest→action.
Security/SIEM and SOAR Sinks
Purpose: regulated retention, correlation, automated playbooks.
- Ingest: only security_event and approved incident_candidate.
- Normalization: ECS/OCSF mapping at the pipeline.
- Controls: immutability where required; signed/audited records; dual-write to SOAR for playbooks.
KPIs: MTTD/MTTR for security intents, false-alert rate, compliance pass rate.
Routing policy (illustrative)
when:
any:
- attr.intent == "incident_candidate"
- attr.intent == "security_event"
then:
redact: ["user.email","headers.authorization","card.number"]
deliver:
- to: agent_incident_hot # AI action sink (p1)
schema: incident.v1
priority: p1
qos: at_least_once
- to: search_hot # Quick investigations
index: "telemetry_hot"
ttl_days: 14
- to: storage_hot
ttl_days: 30
- to: analytics_lake
partition: "dt,service,tenant,intent"
else:
sample: { rate: 0.25, by: "tenant" }
deliver:
- to: analytics_lake
- to: storage_warm
ttl_days: 90
Sink-specific “smart” behaviors
- Storage_hot
- Reject oversized records; push oversized bodies to lake and store pointer.
- Enforce cardinality budgets on labels; auto-normalize.
- Search_hot
- Auto-create materialized views for frequent filters (intent/service/tenant).
- Rate-limit unscoped full-text; require a time bound.
- Analytics_lake
- Late-arriving data merge windows (e.g., 24h).
- Small files compaction (target 128–512MB).
- Agent_incident_hot
- Validate presence of: owner_team, runbook_url, trace_link, blast_radius, risk.
- Refuse if governance.ok != true; emit to quarantine with reason.
“Done right” outcomes
- 30–60% lower storage via shape-before-store & tiering.
- Sub-second ingest→agent on P1 routes with full evidence links.
- Fewer tokens, higher agent precision thanks to compact, enriched records.
- Predictable costs: hot vs warm vs lake clearly bounded.
Instrumentation that agents can use
Guidelines agents can actually use so your telemetry is actionable, small, and safe for real-time decisions.
Modeling agent applications vs agent frameworks
Start with action over archaeology: emit the decision cues an agent needs now (owner, runbook, blast radius, SLO burn), not just raw text. Then make it compact and consistent with short, typed fields over prose; stable keys; low-cardinality labels. Link, don’t dump: include evidence handles (trace/log links) instead of large blobs. Make everything governable by default: never emit PII into agent lanes; add residency/sensitivity tags at source. Always opt for explainable: every “intent” label must be reproducible by a rule or feature; attach rationale.
Add these to every signal at source (logs, metrics, traces, events):
service, env, region, tenant_id,
owner_team, service_tier, runbook_url,
trace_id, span_id, endpoint|operation,
intent, risk, priority, blast_radius,
deploy_sha, feature_flags,
slo_burn_5m, fingerprint, evidence_link[]
Tip: Fill what you can at source; the pipeline can join the rest, but source-side is the most reliable.
Logs: structure for agents (no free-text only)
Emit structured logs with stable fields + optional short message.
Example (JSON log)
{
"ts": "2025-10-08T16:12:03Z",
"service": "checkout",
"env": "prod",
"tenant_id": "acme",
"level": "error",
"code": "PAYMENT_GATEWAY_TIMEOUT",
"endpoint": "POST /pay",
"trace_id": "…",
"fingerprint": "fp_7c41…",
"intent": "incident_candidate",
"risk": "customer_impacting",
"blast_radius": 142,
"slo_burn_5m": 2.7,
"owner_team": "payments-oncall",
"runbook_url": "https://runbooks/payments#timeouts",
"deploy_sha": "a1b2c3d",
"evidence_link": ["https://traces/…"],
"message": "Gateway timeout contacting PSP"
}
Language snippets
- Node (pino/winston)
logger.error({
code: 'PAYMENT_GATEWAY_TIMEOUT',
intent: 'incident_candidate',
risk: 'customer_impacting',
owner_team: process.env.OWNER_TEAM,
runbook_url: process.env.RUNBOOK_URL,
fingerprint: hash(msgTemplate, req.path),
trace_id: getTraceId(),
endpoint: `${req.method} ${req.path}`
}, 'Gateway timeout contacting PSP');
- Python (structlog)
log = structlog.get_logger()
log.error(
"gateway_timeout",
code="PAYMENT_GATEWAY_TIMEOUT",
intent="incident_candidate",
risk="customer_impacting",
owner_team=os.getenv("OWNER_TEAM"),
runbook_url=os.getenv("RUNBOOK_URL"),
trace_id=current_trace_id(),
endpoint=f"{req.method} {req.path}",
)
Do
- Pre-compute fingerprint (template + endpoint).
- Add intent/risk only when you’re ≥80% confident (else omit).
- Keep message short; put details in trace span events.
Don’t
- Emit dynamic IDs in labels (cardinality spikes).
- Put secrets/PII in any agent-visible field.
Traces and attributes that preserve causality
Traces: spans agents can reason about
Must-have attributes
- service.name, deployment.environment, net.peer.name|port, db.system|operation, http.route|method|status_code, feature_flag.*, release.sha.
Span events for decision cues
event.name="retry_exhausted" attrs={retries:3, policy:"exponential"}
event.name="rate_limited" attrs={provider:"psp", limit_window:"1m"}
event.name="circuit_open" attrs={breaker:"psp-write"}
Link logs↔traces
- Inject W3C traceparent in HTTP, gRPC, message headers.
- When logging, copy trace_id/span_id from context (MDC/ThreadLocal).
LLM/Agent spans (observability for AI calls)
ai.model.name, ai.request.id, ai.prompt.tokens, ai.completion.tokens,
ai.latency.ms, ai.tool.invocations, ai.safety.filtered=true|false
Metrics: small, SLO-oriented, label-budgeted
Names
- http.server.errors_total, http.request.duration_ms, queue.depth, worker.restarts, ai.calls_total, ai.tool.failures_total
Labels (strict)
- service, env, region, tenant_id, endpoint|route, version, canary=true|false
Emit derivatives for agents
- Burn rate (slo:burn_rate_5m)
- Error rate windows (http_5xx_rate_1m)
- Availability (availability_ratio_5m)
OpenTelemetry metric example (Go)
errCounter, _ := meter.Int64Counter("http.server.errors_total")
errCounter.Add(ctx, 1, attribute.String("service","checkout"),
attribute.String("endpoint","POST /pay"),
attribute.String("tenant_id", tenant),
attribute.String("env","prod"))Business / Platform Events: normalize for triggers
Standardize to event.v1 (compact, typed):
type: deploy_started|deploy_finished|feature_flag_changed|billing_alert|security_alert
subject: "checkout"
actor: "cd-system"
severity: info|warning|critical
tenant_id: "acme"
trace_id: "…"
details: { flag:"psp_v2", old:"off", new:"on" }
These feed agent context (what changed) and often explain anomalies.
Capturing agent tasks, tools, prompts, outcomes, and errors
AI/Agent-specific telemetry (make your agents inspectable)
Instrument both the agent runtime and the tools/functions it calls.
Agent decision record (incident.v1)
{
"symptom_fingerprint": "fp_7c41…",
"hypothesis": "psp timeout after deploy",
"confidence": 0.71,
"pre_checks": ["error_rate>5%", "deploy_sha changed", "blast_radius>50"],
"proposed_action": "rollback",
"rollback_plan": "deploy a1b2c2",
"evidence_link": ["https://traces/…","https://logs/…"],
"rationale": "spike aligns with deploy; circuit breaker open"
}
Tool call audit (every action)
tool.name, tool.args.hash, tool.result.status, tool.latency.ms,
guardrail.passed=true|false, approver=user|auto, change_id
LLM gateway instrumentation
llm.model, llm.provider, llm.temperature, tokens.prompt, tokens.completion,cache.hit=true|false, safety.filter=true|false, cost.usd
Minimal semantic conventions (stick to these names)
- intent: incident_candidate|security_event|usage_signal|cost_regression|none
- risk: customer_impacting|internal|security|compliance
- priority: p1|p2|p3
- blast_radius: integer (affected users/tenants)
- evidence_link: array of URLs/handles (no payloads)
- fingerprint: stable hash for dedupe
- slo_burn_5m: float
Keep enums tight; reject unknowns at CI.
Quick copy-paste: HTTP header propagation (Node/Express)
app.use((req, res, next) => {
const tp = req.headers['traceparent'] || createTraceparent();
res.setHeader('traceparent', tp);
logger.setBindings({ trace_id: traceIdFrom(tp) });
next();
});
Quick copy-paste: span events for retries (Go, OTel)
span.AddEvent("retry_exhausted",
trace.WithAttributes(
attribute.Int("retries", 3),
attribute.String("policy", "exponential"),
))Context over volume
Traditional observability was built on volume-first thinking: Collect as many logs, metrics, traces, and events as possible, store them all in centralized platforms, and query later when something goes wrong. But costs explode as raw data floods storage and rehydration systems, noise drowns out signal, and latency creeps in—store-first pipelines add lag before signals reach responders.
With AI agents in the loop, telemetry is no longer just for retrospective dashboards—it’s a live input to decision-making. Agents need clean, structured context at ingest. Overfeeding raw data leads to hallucinations, false actions, and wasted tokens. But right-sizing telemetry results in agents that can act with confidence, speed, and precision.
Instead of flooding agents with raw records, telemetry pipelines deliver enriched, shaped context:
How to Engineer Context Over Volume
- Enrich at ingest: attach owner_team, deploy_sha, runbook_url, trace_id.
- Redact + tokenize early: protect PII before agents see it.
- Aggregate repetitive signals: convert repeated logs into log-derived metrics.
- Derive golden signals: pre-compute error rates, SLO burn, anomaly flags.
- Label with intent: incident_candidate, security_event, usage_signal → drives routing.
- Route by priority: P1 signals flow in real time; batch the rest.
The Payoff
- Lower cost: fewer raw records stored and rehydrated.
- Less noise: agents act on true signals, not clutter.
- Faster response: sub-second ingest→agent loops on P1 incidents.
- Greater trust: every agent action backed by structured evidence and rationale.
Agent-aware telemetry means trading volume for context. Instead of “store it all,” pipelines shape data into decision-ready context so agents—and humans—can act fast, safe, and cost-effectively.
Deduplication, sampling, and cardinality controls
Repetitive log lines, stack traces, and retries swamp both humans and AI agents.
- Impact without control:
- Wasted storage (10k identical errors).
- Agents hallucinate importance (“10k errors = 10k incidents”).
- Higher latency for queries and searches.
Deduplication Strategies:
- Fingerprinting: hash on template + service + code + endpoint → stable signature for repeated errors.
- Time-window collapsing: count duplicates over 1m/5m windows → emit single record with count=N.
- Span/trace linking: group identical logs under one trace span → agents see event with evidence, not flood.
Agent-aware effect: Agents get one enriched error fingerprint + context (owner, runbook, blast radius), not thousands of raw lines.
Firehosing every record drowns systems and agents.
- Impact without control:
- Ingest and storage costs skyrocket.
- Agents overfit to noise, miss rare but critical anomalies.
Sampling Approaches:
- Random sampling: drop percentage of low-value records.
- Dynamic sampling: adjust rates during spikes; keep more when error rate rises.
- Intent-aware sampling: never sample incident_candidate or security_event, but aggressively downsample debug/info.
- Tail-based sampling (for traces): keep only anomalous/errored traces; drop normal ones.
Agent-aware effect: Agents see all the critical signals and a representative slice of the rest — lean, fast, and trustworthy.
Metrics and logs with unbounded labels (user IDs, UUIDs, IPs, stack hashes) cause explosions.
- Impact without control:
- Metrics stores like Prometheus/OTel Collector blow up memory.
- Agents waste tokens parsing fields that don’t matter.
- Queries slow to a crawl.
Control Strategies:
- Drop or normalize high-cardinality fields: e.g., replace user IDs with user.tier=gold|silver|bronze.
- Tokenization: deterministic hash for joinability, but prevents explosion.
- Label budgets: enforce a max number of values per label (e.g., endpoint <= 1k).
- Cardinality guards: CI tests + live monitors flagging when a label crosses safe thresholds.
Agent-aware effect: Agents get stable, compressed context (tiers, categories, fingerprints) instead of drowning in one-off IDs.
How They Work Together in an Agent-Aware Pipeline
- Deduplication reduces raw noise → collapse repeats.
- Sampling shapes overall flow → keep only what matters.
- Cardinality control prevents structural overload → stop unbounded key explosion.
Together they:
- Shrink token and storage footprint.
- Preserve signal over noise.
- Enable real-time action (sub-second hot lanes).
- Lower costs (storage, rehydration, agent prompts).
Example OTEL Collector Config Snippets
Deduplication via fingerprinting
processors:
transform/fingerprint:
log_statements:
- set(attributes.fingerprint, sha256(template(body)))
Intent-aware sampling
processors:
probabilistic_sampler/errors:
sampling_percentage: 100 # keep all errors
include:
match_type: strict
attributes:
intent: incident_candidate
probabilistic_sampler/info:
sampling_percentage: 10 # keep 10% of info/debug logs
Cardinality drop/normalize
processors:
attributes/drop-high-card:
actions:
- key: user.id # drop raw IDs
action: delete
- key: request_id # replace with fingerprint
action: hash
KPIs for Success
- Deduplication: % reduction in repeated events; MTTR improved by fewer false tickets.
- Sampling: % dropped events by intent; p95 ingest→agent latency (hot lane).
- Cardinality: max series per metric name; label value growth rates; agent token/decision size.
Deduplication, sampling, and cardinality controls are the governors that make agent-aware telemetry cost-efficient, precise, and safe. Instead of agents drowning in raw volume, they act on compressed, structured context that’s tailored to decision-making.
Enrichment with identities, environments, and policies
Enrichment with identities, environments, and policies is exactly what turns telemetry from raw exhaust into agent-ready context. Here’s a structured breakdown:
Agents don’t just need data - they need context that explains “who, where, and under what rules.”
- Identities: Who generated or owns this signal? (service, team, tenant, user class)
- Environments: Where did it happen? (region, stage, deployment)
- Policies: What rules govern this data? (retention, redaction, compliance, priority)
This enrichment allows AI agents (and humans) to make safe, precise, and fast decisions.
Attach “who” the telemetry belongs to, so agents can route and act responsibly.
- Service & Workload Identity:
- service.name, workload, owner_team
- Derived from deployment metadata or topology graph
- Tenant / Customer Identity:
- tenant_id, account_tier, SLA_level
- Used for blast radius analysis and escalation rules
- User Identity (governed):
- Instead of raw PII (user emails/IDs), enrich with categories → user.tier=gold|silver, authn_method=oauth|sso
Example:
{
"service": "checkout",
"owner_team": "payments-oncall",
"tenant_id": "acme",
"sla_level": "gold"
}
Attach “where” and “under what conditions” the telemetry originated.
- Runtime Environment: env=prod|staging|dev, region=us-east-1, cluster=k8s-prod-3
- Deployment State: deploy_sha, image_tag, feature_flag={"newCheckout":true}
- Topology Links: upstream/downstream dependencies, blast radius estimate
- Cloud/Infra Context: cloud.provider=aws, zone=us-east-1a, instance_type=m6i.large
Example:
{
"env": "prod",
"region": "us-east-1",
"deploy_sha": "a1b2c3d",
"feature_flags": ["newCheckout:true"],
"service_tier": "tier1"
}
Policy Enrichment
Attach “what rules apply” so governance and actions respect constraints.
- Governance Tags:
- sensitivity=PII|PCI|internal
- residency=us|eu
- retention_days=30
- Routing & Priority Rules:
- intent=incident_candidate → goes to hot lane
- priority=p1|p2|p3
- Access & Masking Rules:
- masking=redacted:card.number
- policy_version=2025-10-08.3
Example:
{
"intent": "incident_candidate",
"priority": "p1",
"sensitivity": "PCI",
"retention_days": 30,
"policy_version": "2025.10.08"
}
How They Work Together
Imagine a single enriched record:
{
"ts": "2025-10-08T16:12:03Z",
"service": "checkout",
"owner_team": "payments-oncall",
"env": "prod",
"region": "us-east-1",
"deploy_sha": "a1b2c3d",
"tenant_id": "acme",
"sla_level": "gold",
"intent": "incident_candidate",
"priority": "p1",
"sensitivity": "PCI",
"runbook_url": "https://runbooks/payments#timeouts",
"trace_id": "abc123",
"fingerprint": "fp_9f1d…",
"slo_burn_5m": 2.3,
"policy_version": "2025-10-08.3"
}
This gives an AI agent all the context it needs to:
- Route the signal to the right responder or remediation workflow.
- Respect compliance rules (don’t expose PII).
- Decide urgency (p1 gold-tier customer in prod).
- Provide rationale for action (deploy just changed, SLO burn >2.0).
Where to Enrich in the Pipeline
- At Source (best): Add service, env, deploy_sha directly from app/runtime.
- At Edge Collector: Attach region, cluster, cloud.provider.
- At Policy Engine: Join with CMDB/topology to attach owner_team, runbook_url, sensitivity.
- Before Routing: Label intent, priority, and apply policy_version.
KPIs That Prove It’s Working
- % of signals with owner_team and runbook_url (aim ≥95%).
- % of PII-free agent payloads (governance pass rate ≥99.9%).
- Time-to-resolve reduction from enriched vs. unenriched alerts.
- Reduction in false positives from intent labeling.
- Agent token size per decision (smaller with richer enrichment).
Enrichment with identities, environments, and policies makes telemetry decision-ready. Instead of drowning in raw volume, agents see who, where, and under what rules — the context needed to act fast, safe, and confidently.
Live tail for rapid feedback and MTTR gains
Here’s a practical, no-fluff playbook to make Live Tail a force multiplier for rapid feedback and MTTR gains in an agent-aware telemetry stack.Live Tail is streaming a shaped subset of fresh telemetry (logs/events and key metrics/traces as links) to humans and AI agents with:
- sub-second latency,
- noise controls (dedupe, sampling),
- safe enrichment (owner, runbook, deploy, tenant),
- and intent-aware routing (P1 hot lane).
Goal: shorten the loop from symptom → insight → action during deploys, incidents, and experiments.
This impacts MTTR because time-to-signal (TTS) drops from minutes to seconds, hypothesis validation happens in-flow, action confidence rises, and ticket churn falls.
Design principles (the “smart tail”)
- Shape before stream: parse → redact → enrich → fingerprint → intent → then stream.
- Hot lane only: tail just the P1/P2 intents; batch the rest.
- Compact payloads: show fingerprints + counters + evidence links, not full blobs.
- Guardrails on by default: PII scrub, residency, rate caps, backpressure.
- Two audiences, one feed: humans (UI/CLI) and agents (schema’d stream) see the same truth.
Typical Live-Tail flows (where it shines)
- Deploy & feature flips: watch for error-rate bumps, latency drifts, canary fallout.
- Incident triage: confirm impact, scope (tenant/SLA), blast radius, correlated changes.
- Runbook execution: follow remediation steps and verify success in real time.
- Experimentation: immediate product/LLM behavior feedback without log archaeology.
What to stream (minimal, decision-ready)
For each event in the tail:
ts, service, env, region, tenant_id,
owner_team, runbook_url, deploy_sha, feature_flags[],
intent, priority, fingerprint, count_1m,
http_5xx_rate_1m | slo_burn_5m,
trace_link[], search_link- Filters: service/env/tenant/intent/fingerprint.
- Sticky pins: keep key fingerprints on top.
- Burst guard: collapse repeats (show counters).
- One-click jumps: trace view, runbook, rollout dashboard.
- Redaction badges: show what was masked (e.g., email, token)—not the values.
Smart Tail API (agents)
- Stream topic like tail.p1 with incident.v1 envelopes:
- symptom_fingerprint, blast_radius, slo_burn_5m, evidence_link[], policy_version.
- Agents reply to actions.v1 with rationale + links (audited).
Concrete configs (illustrative)
1) OTEL Collector — Live Tail pipeline (P1)
receivers:
otlp: { protocols: { http: {}, grpc: {} } }
processors:
transform/normalize: {}
attributes/redact:
actions:
- key: user.email
action: delete
- key: headers.authorization
action: delete
transform/enrich:
log_statements:
- set(attributes.owner_team, cache_lookup("owner_team", resource.attributes["service.name"]))
- set(attributes.runbook_url, cache_lookup("runbook", resource.attributes["service.name"]))
- set(attributes.fingerprint, sha256(template(body)))
transform/derive:
log_statements:
- set(attributes.intent, when(attributes.level=="error","incident_candidate","none"))
filter/intent:
logs:
include:
match_type: strict
attributes:
- key: intent
value: incident_candidate
groupby/fingerprint: {} # collapses and counts within a short window
routing/hotlane:
attribute_source: attribute
from_attribute: attributes.intent
table: { incident_candidate: [ p1_tail, p1_store ] }
exporters:
kafka/p1_tail: { brokers: ["kafka:9092"], topic: "tail.p1" } # UI/agents
clickhouse/p1_store: { endpoint: "http://clickhouse:8123" } # for rewind
service:
pipelines:
logs:
receivers: [ otlp ]
processors: [ transform/normalize, attributes/redact, transform/enrich, transform/derive, filter/intent, groupby/fingerprint, routing/hotlane ]
exporters: [ kafka/p1_tail, clickhouse/p1_store ]
2) Fluent Bit (edge) — follow + redact + forward
[INPUT]
Name tail
Path /var/log/app/*.log
Tag app.*
DB /var/flb/state.db
Refresh_Interval 1
Read_From_Head Off
[FILTER]
Name modify
Match app.*
Remove_key user.email
Remove_key headers.authorization
[OUTPUT]
Name http
Match app.*
Host otel-collector
Port 4318
URI /v1/logs
Format json
3) CLI for humans (K8s examples you already use)
# App-specific stream with intent filter
kubectl logs deploy/checkout -c app -f | jq -c 'select(.intent=="incident_candidate")'
# Tail traces by service (via your tracing backend)
tempo-cli tail --service checkout --error-only
# Tail summarized fingerprints (from a materialized feed)
kafkacat -C -b kafka:9092 -t tail.p1 | jq -r '.service,.fingerprint,.count_1m,.slo_burn_5m'Cost control without losing signal
Here’s a blueprint for cost control without losing signal in an agent-aware telemetry pipeline. It’s about keeping telemetry useful for agents and humans without drowning in storage bills or wasted compute.
Right-time retention and tiering strategies
Problem: “Store first, analyze later” creates runaway storage and rehydration costs.
Solution: Apply filters, enrichment, and compaction at ingest:
- Drop obvious noise (debug logs, health checks).
- Collapse repeats into fingerprints + counters.
- Convert logs → metrics where repetition is high.
- Enrich with context (owner, runbook, tenant) so you can store less, but know more.
You cut volume before it hits expensive storage.
Deduplication and Fingerprinting
- Hash on message_template + code + endpoint → stable signature.
- Collapse 10k identical errors into 1 event + count=10k.
- Store fingerprints + evidence links; let agents retrieve detail only if needed.
Effect: 70–90% fewer redundant records, but signal stays intact.
Intent-Aware Sampling
- Keep 100% of critical paths: incident_candidate, security_event, SLA-tier customers.
- Downsample low-value logs: drop 90% of debug/info in prod.
- Tail-based trace sampling: keep anomalous traces, drop healthy ones.
- Dynamic sampling: scale retention during spikes, revert when stable.
Effect: Hot lanes stay full fidelity; background noise is controlled.
Cardinality Controls
- Enforce budgets on metric labels (endpoint <= 1k, tenant_id <= 10k).
- Normalize high-card fields: user.id → user.tier.
- Tokenize deterministically: hashed values for joinability without explosion.
- CI tests + live alerts for new label blowups.
Effect: Avoids runaway Prometheus/OTel collector costs and keeps queries fast.
Route only what matters to long-term stores
Smart Storage Tiering
- Hot store (14–30d): fast, indexed, sub-second queries (ClickHouse, Quickwit).
- Warm store (90–180d): compressed Parquet/ORC in lake.
- Cold archive (>180d): S3/Glacier with selective rehydration.
- TTL by intent: 14d for incident_candidate, 90d for compliance, 1y for audit logs.
Effect: Keep “decision data” close, archive the rest cheaply.
Guardrails for bursty agent workloads
Governed Routing (don’t send everything everywhere)
- Route only enriched, intent-labeled slices to AI agents.
- Keep verbose raw data in cold/warm tiers, but out of real-time flows.
- Apply policy tags: sensitivity=PCI, retention=30d, intent=p3.
Effect: Agents process only decision-ready slices, not wasteful raw streams.
Feedback Loops & Previews
- Cost diff in PRs: every pipeline change shows delta in volume + $ impact.
- Shadow/preview mode: simulate routing/sampling before rollout.
- Monitor KPIs: GB/day ingested vs. retained, agent token/decision size, FP/FN rate.
Effect: Cost and fidelity tuned continuously, not just once.
KPIs That Prove Balance
- Ingest → storage reduction: 40–70% less data without losing P1/P2 signals.
- Rehydration frequency: trending down; more questions answered by enriched context.
- Agent token size/decision: reduced 30–60% with same or better accuracy.
- MTTR: down 20–40% thanks to Live Tail + enriched, deduped signals.
Cost control isn’t about cutting data—it’s about shaping it into context. Deduplication, intent-aware sampling, and cardinality controls keep the signal intact while slashing storage, rehydration, and agent token costs. You end up with leaner pipelines, lower bills, and faster, safer agent actions.
Governance, safety, and compliance
We have a guide to governance, safety, and compliance in an agent-aware telemetry pipeline with the controls that make agents trustworthy, safe, and compliant when they act on real-time signals.
Governance is critical for agents because they act in the loop: They don’t just analyze; they trigger remediation, escalate tickets, flip flags. Blind ingestion is dangerous: If raw telemetry leaks PII or violates residency, an agent may expose it or act illegally. Also, trust equals adoption: Engineers and execs won’t trust AI automation unless telemetry has built-in guardrails. Governance isn’t an afterthought — it’s the substrate that keeps agents reliable, safe, and auditable.
Core Governance Controls
a) Schema Validation
- Enforce strict schemas (log.v1, metric.v1, trace.v1, event.v1, incident.v1).
- Reject or quarantine malformed records → prevent garbage from reaching agents.
b) Policy Versioning
- Tag every record with policy_version.
- Enable blue/green policy rollouts and rollbacks if a governance change breaks flows.
c) Audit and Provenance
- Attach processors[], redaction_hash, and source_cluster metadata.
- Keep immutable audit trails for who shaped what before an agent saw it.
Safety Mechanisms
a) Redaction and Tokenization (PII/Secrets)
- Delete or deterministically hash emails, auth tokens, PANs, IPs.
- Run both regex-based + ML-assisted detectors (shadow first, fail-closed later).
b) Fail Modes (Fail-Closed vs Fail-Open)
- Security/PCI lanes → fail-closed (drop if unverified).
- Analytics lanes → fail-open (buffer if governance check fails).
c) Rate and Blast Radius Guards
- Throttle per tenant/service.
- Enforce “blast radius tags” so agents can’t remediate outside scope.
d) Rationale and Approval Hooks
- Require agent actions to include: symptom, hypothesis, confidence, evidence_link[].
- Optionally require human approval for destructive actions (rollback, scaling down).
Compliance Alignment
a) Residency & Data Zones
- Enforce residency=us|eu|apac tags at ingest.
- Route to compliant sinks per region.
b) Retention and Right-to-Erasure
- TTL by intent (e.g., P1 incidents 30d, audit 1y).
- Support selective deletion for user data (user.erase=true).
c) Frameworks Standards
- Map telemetry to OCSF or ECS for SIEM interoperability.
- Enforce SOC 2 / ISO 27001 controls: immutability, access logs, least privilege.
- Tag sensitive signals with GDPR/PCI/HIPAA categories at ingest.
Where Governance Lives in the Pipeline
- Source: Service-level lint to block unsafe log keys (e.g., password).
- Edge Collector: Early PII redaction, residency tags.
- Policy Engine: Schema validation, enrichment joins, sensitivity tagging, intent labeling.
- Before Sink: Defense-in-depth redaction; compliance routing (e.g., EU-only sinks).
- Control Plane: Policy repo, approvals, shadow testing, audit dashboards.
KPIs That Show It’s Working
- Redaction recall/precision: ≥ 99.5% sensitive-field detection.
- Governance pass rate: ≥ 99.9% records compliant before agent consumption.
- Policy drift: < 5% configs out of sync across regions.
- Residency violations: 0 per quarter.
- Agent safe-action rate: % of actions with rationale + evidence; < 1% rollback overrides.
Example Enriched, Governed Event
{
"ts": "2025-10-08T16:12:03Z",
"service": "checkout",
"env": "prod",
"region": "eu-central-1",
"tenant_id": "acme",
"intent": "incident_candidate",
"priority": "p1",
"sensitivity": "PCI",
"owner_team": "payments-oncall",
"runbook_url": "https://runbooks/payments#timeouts",
"fingerprint": "fp_9f1d…",
"trace_id": "abc123",
"policy_version": "2025-10-08.3",
"provenance": {
"processors": ["normalize@1.4", "redact@2.1", "enrich@0.9"],
"source_cluster": "edge-eu1"
}
}
Agent-aware telemetry pipelines must enforce governance, safety, and compliance at ingest, not after the fact. That means schema validation, redaction, residency controls, policy versioning, and auditable provenance. With these in place, agents can act quickly and safely, giving organizations confidence to automate without regulatory or trust failures.
PII detection, masking, and policy enforcement
This is the layer that keeps signals safe for both human and agent consumption, without losing the context that makes them useful.
A stray email address or token in a log isn’t just a compliance issue — it could be exposed in an agent suggestion, prompt, or remediation. GDPR, HIPAA, PCI-DSS require strict handling of personal and sensitive data. And engineers and execs won’t trust AI-powered observability if raw PII leaks through. PII control has to be in-stream, automated, and policy-driven — not something handled “after storage.”
PII Detection Techniques
Pattern-Based (fast path)
- Regex + rule-based detectors for common PII:
- Emails → (?i)\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,}\b
- Credit cards (Luhn check) → \b(?:\d[ -]*?){13,19}\b
- IPv4/IPv6 addresses, SSNs, phone numbers
- Cheap, deterministic → ideal for hot lanes.
Dictionary/Keyword Matching
- Trigger on keys like ssn, dob, auth, password, credit_card.
- Integrated into schema validation to prevent unsafe fields.
ML/NLP-Assisted (shadow mode)
- Models classify text spans (log bodies, events) for PII.
- Use in batch or as a safety net, not the primary hot-path detector (too slow for sub-second).
Masking and Tokenization
Masking (obfuscate part of the data)
- Show only last 4 digits → •••• 1234
- Replace with type token → [EMAIL], [SSN]
- Useful for human readability in live tail / dashboards.
Tokenization (stable, non-reversible hashes)
- Deterministic HMAC of value → tok_f7c9a…
- Preserves ability to correlate across systems without revealing raw PII.
- Ideal for tenant IDs, hashed emails, device IDs.
Redaction (full removal)
- Delete field/value entirely → safest but least context.
- Use for auth tokens, session cookies, secrets.
Policy Enforcement
Inline Governance Rules
- Tag records with sensitivity=PII|PCI|PHI as soon as detected.
- Enforce routing by policy:
- PCI → storage with encryption + short TTL.
- PII → masked for agent consumption.
- Internal only → route to low-sensitivity sinks.
Fail Modes
- Fail-closed for hot lane: if PII not masked, drop or quarantine record.
- Fail-open for analytics: forward with redacted body, log violation.
Policy Versioning
- Every record carries policy_version.
- Allows audit and rollback if a masking policy was misconfigured.
Compliance Alignment
- Residency: EU PII must not leave EU sinks.
- Retention: retention_days=30 for sensitive classes.
- Right-to-erasure: track tokenized identifiers for selective deletion.
Where It Runs in the Pipeline
- At Source: Developers lint out forbidden keys (password, ssn) during log calls.
- At Edge Collector: Fast regex/dictionary detection, masking, and redaction.
- At Policy Engine: Enrichment with sensitivity tags, tokenization, policy routing.
- Before Sinks/Agents: Defense-in-depth masking + compliance validation.
Example Configs
OTEL Collector — Attribute Redaction
processors:
attributes/redact:
actions:
- key: user.email
action: delete
- key: headers.authorization
action: delete
- key: card.number
action: delete
Transform for Masking
processors:
transform/mask:
log_statements:
- set(attributes.card_last4, substring(attributes.card.number, -4))
- delete(attributes.card.number)
Kafka Streams — Deterministic Tokenization
rec.put("tenant_id_tok", hmacSha256(SECRET, rec.get("tenant_id")).substring(0,16));
rec.remove("tenant_id");
Example Governed Record
{
"ts": "2025-10-08T16:12:03Z",
"service": "checkout",
"tenant_id": "tok_a7f21…",
"env": "prod",
"intent": "incident_candidate",
"priority": "p1",
"sensitivity": "PCI",
"runbook_url": "https://runbooks/payments#timeouts",
"policy_version": "2025-10-08.3",
"provenance": {
"processors": ["redact@2.1", "mask@1.2"],
"source_cluster": "edge-us1"
}
}In an agent-aware pipeline, PII detection, masking, and policy enforcement are in-stream, layered, and auditable. You don’t trade off signal — you shape telemetry into safe, enriched context that agents can use for decisions without exposing sensitive data.
Transparent lineage for audits
Here’s how to think about transparent lineage for audits in an agent-aware telemetry pipeline — the ability to prove, with evidence, what data was ingested, how it was transformed, which policies applied, and what agents saw or did with it.
- Trust in automation → auditors, SREs, and execs need proof that agents acted on governed, compliant signals.
- Regulatory requirements → GDPR, HIPAA, PCI, SOC 2 demand traceability from data source → transformation → action.
- Incident forensics → engineers must know exactly what an agent saw and why it acted.
Bottom line: lineage makes AI-driven operations auditable, explainable, and defensible.
What Transparent Lineage Includes
a) Provenance Metadata
Every record carries:
- source: collector/region/service/tenant
- processors[]: sequence of pipeline stages (normalize@1.4, redact@2.1, enrich@0.9)
- policy_version: active policy config at time of processing
- fingerprint: stable dedupe key for cross-system joins
b) Transformation Chain
- Each pipeline stage logs: input → operation → output
- Example:
- Input: user.email="alice@example.com"
- Operation: redact → delete(user.email)
- Output: (field removed, hash persisted in audit log)
c) Agent Context Exposure
- Snapshot of the enriched, filtered record as delivered to the agent.
- Record of any evidence links (trace, log, metrics).
- What the agent did not see (e.g., masked PII) is as important as what it did.
d) Action Trail
- Agent rationale: symptom, hypothesis, confidence, evidence_link.
- Proposed/remediated action: rollback, scale, suppress alert.
- Approval path: auto vs. human-approved.
How to Implement
Inline Metadata Injection
Add a provenance block to every telemetry record:
"provenance": {
"source": "otel-edge-us1",
"processors": ["normalize@1.4","redact@2.1","enrich@0.9"],
"policy_version": "2025-10-08.3",
"timestamp": "2025-10-08T16:12:03Z"
}
Audit Stream / Dead-Letter Queue
- Side-channel topic (e.g., audit.telemetry) records full lineage traces.
- Stores pre/post values for sensitive ops (like PII redaction).
- Quarantined records (schema violations, failed redactions) land here too.
Immutable Storage for Audit Trail
- Append-only, WORM (write-once, read-many) storage (e.g., S3 Glacier Vault Lock).
- Cryptographic signatures for record authenticity.
- Indexed by fingerprint + policy_version.
Traceability Links
- Correlate telemetry with incident/agent actions using IDs: trace_id, incident_id, action_id.
- Let auditors replay “what happened” end-to-end.
KPIs for Transparent Lineage
- Audit completeness: ≥ 99.9% of records carry provenance metadata.
- Policy coverage: 100% of enriched records tagged with policy_version.
- Action traceability: every agent action has rationale + evidence snapshot.
- Zero gaps: no untracked transformations in production flows.
Example: Agent Action Audit Entry
{
"incident_id": "inc-4321",
"symptom_fingerprint": "fp_9f1d…",
"agent_input": {
"intent": "incident_candidate",
"service": "checkout",
"owner_team": "payments",
"slo_burn_5m": 2.4,
"policy_version": "2025-10-08.3"
},
"agent_rationale": "Error spike aligned with new deploy; blast radius >50 tenants",
"action": "rollback",
"approver": "auto",
"timestamp": "2025-10-08T16:14:10Z",
"provenance": {
"processors": ["normalize@1.4","redact@2.1","enrich@0.9"],
"source": "otel-edge-us1"
}
}
This shows what the agent saw, why it acted, and under which policy version.
Transparent lineage for audits means every telemetry record and agent action carries a cryptographically verifiable trail of its origins, transformations, policies, and outcomes. It ensures AI-driven operations stay explainable, compliant, and trustworthy.
Human in the loop review points
Human-in-the-loop (HITL) review points are the guardrails that keep agent-aware telemetry pipelines safe, compliant, and trustworthy. Think of them as “circuit breakers” where humans step in to verify, approve, or override before automation proceeds.
Why HITL Review Points Matter
- Agents act in real time → they remediate, escalate, or suppress signals.
- Not all signals are equal → P1 incidents or PCI data may require a human check.
- Trust & adoption → teams are more likely to allow agent automation if they know there are clear “pause and review” steps.
Where to Insert Review Points
a) Data Governance & Policy Checks
- When: A pipeline detects PII/PHI, residency conflict, or schema violation.
- Human role: Validate redaction rules, approve quarantine releases, or update policy.
- Why: Prevent unsafe data from leaking into agent contexts.
b) Incident Classification & Escalation
- When: An agent labels an event as incident_candidate with medium confidence (e.g., 0.6–0.8).
- Human role: Confirm severity, reclassify if necessary, choose escalation path.
- Why: Avoid false positives turning into noisy incidents.
c) Remediation Actions
- When: High-impact changes (rollback, feature flag off, scaling down production).
- Human role: Review agent rationale + evidence, approve/reject action.
- Why: Reduce risk of cascading outages from overconfident automation.
d) Compliance & Audit Triggers
- When: Telemetry tagged as sensitivity=PCI|PHI|GDPR, retention override, or cross-region transfer.
- Human role: Approve or reject data move/retention exception.
- Why: Ensure adherence to legal obligations.
e) Feedback Loops for Learning
- When: An agent takes an action (e.g., ticket suppression, correlation, remediation).
- Human role: Mark it “helpful / not helpful.”
- Why: Provides reinforcement for improving classification and action models.
How HITL Review Points Work in Practice
- Confidence thresholds:
- ≥ 0.9 → agent acts autonomously.
- 0.6–0.9 → agent proposes action; human approval required.
- ≤ 0.6 → agent escalates for manual triage.
- Two-channel delivery:
- Agent output → structured rationale (symptom, hypothesis, confidence, evidence_links).
- Human UI/Slack integration → Approve/Reject with one click, feedback stored in audit trail.
- Escalation timers:
- If no human response within SLA (e.g., 2 minutes), default to safest option (hold/suppress, not remediate).
Example Review Record
{
"incident_id": "inc-4321",
"symptom_fingerprint": "fp_9f1d…",
"agent_confidence": 0.72,
"agent_rationale": "Spike in 5xx rate post-deploy; blast radius > 50 tenants",
"proposed_action": "rollback",
"evidence_links": ["https://traces/...", "https://logs/..."],
"review_point": "remediation_approval",
"human_decision": "approved",
"reviewer": "oncall@sre.team",
"timestamp": "2025-10-08T16:15:10Z"
}Human-in-the-loop review points ensure that agents act fast where safe, and pause where risky. By inserting checkpoints at governance, classification, remediation, and compliance stages, teams keep automation auditable, trusted, and safe — without losing speed or signal.
Getting started with Mezmo Active Telemetry
Here’s a getting started guide for Mezmo Active Telemetry framed around how you move from “store everything, analyze later” into real-time, context-ready telemetry pipelines that power both humans and AI agents.
Most observability today is passive: collect → store → query → (maybe) act.
Active telemetry flips this: shape → enrich → govern → act in the flow.
With Mezmo Active Telemetry, your pipeline doesn’t just ship data — it:
- Filters noise before it ever hits storage.
- Normalizes and enriches signals with identities, environments, and policies.
- Redacts PII and enforces governance inline.
- Routes intent-tagged data to the right systems (storage, search, SIEM, AI agents).
- Provides real-time streams for human and AI decision-making.
There are a number of key principles to begin with:
Shape at Ingest
- Parse and normalize logs/metrics/traces/events.
- Drop debug or heartbeat clutter.
- Apply deduplication and intent-aware sampling.
Enrich for Context
- Join service → owner → runbook.
- Attach deploy/feature-flag metadata.
- Add tenant/SLA tier to evaluate blast radius.
Govern and Protect
- Detect and redact PII (emails, tokens, PANs).
- Tag records with sensitivity, residency, policy_version.
- Enforce fail-closed rules for security and compliance paths.
Route by Intent
- Classify signals into incident_candidate, security_event, usage_signal, cost_regression.
- Deliver P1 “hot lane” data sub-second to responders or agents.
- Batch and compress lower-priority telemetry for analytics/storage.
Audit & Lineage
- Track every transformation and policy applied.
- Provide transparent lineage for audits and compliance.
- Capture what the agent actually saw before acting.
Teams should leverage the following Mezmo capabilities:
- Telemetry Pipeline: low-latency stream processing for shaping, enrichment, governance.
- Active Routing: intent-aware rules to deliver the right data to the right sink.
- Data Policies: declarative configs for masking, sampling, retention, and routing.
- Log Analysis & Visualization: rapid feedback for deploys, incidents, and experiments.
- Integrations: sinks to ClickHouse, Elasticsearch, Quickwit, SIEM, data lakes, and AI agent frameworks.
- Audit & Compliance Layer: provenance, lineage, and policy version tracking.
Templates for agent task spans and events
- Connect Sources
- Deploy Mezmo collectors or connect Fluent Bit / OTEL Collector.
- Start with one service or cluster to avoid overloading.
- Apply Basic Policies
- Drop debug logs in prod.
- Mask common PII fields (email, tokens).
- Normalize service + environment labels.
- Set Up Enrichment
- Add owner team + runbook links via CMDB/topology.
- Tag records with deploy SHA + feature flags.
- Define Intent Rules
- Example: if http_5xx_rate_1m > 5% and tier=tier1 → intent=incident_candidate.
- Route these to Live Tail + agent sink.
- Activate Smart Sinks
- Hot store (short retention for incidents).
- Search (Quickwit/Elastic for developer investigations).
- Lake (Snowflake/BigQuery for analytics).
- Agent lanes (incident.v1 schema → AI agent).
- Monitor & Iterate
- Watch volume reduction vs. baseline (cost savings).
- Track MTTR with Live Tail enabled.
- Validate governance pass rate in audit dashboards.
Quick wins in hours not weeks
- Noise cut: drop 30–60% of logs by filtering health checks, retries, debug.
- Cost savings: reduce storage/rehydration by shaping data at ingest.
- Faster incident triage: Live Tail with enriched signals lowers MTTR by 20–40%.
- Agent safety: agents see context-ready, PII-safe inputs, reducing false positives.
Measuring success with MTTR, cost per GB, and actionability
- Ingest volume vs. post-policy volume.
- % of records with owner/runbook enrichment.
- Governance pass rate (PII masking, residency compliance).
- Hot-lane latency (ingest → routed agent/human tail).
- MTTR delta (before vs. after Active Telemetry).
- Agent token size per decision (should shrink as enrichment improves).
Getting started with Mezmo Active Telemetry means moving from “collect and hope” to shape, enrich, govern, and act in real time. Begin with one service, apply basic filters + enrichment, route incident candidates to Live Tail + agents, and measure your volume reduction and MTTR improvements.
Case snapshot
Before and after signal-to-noise
A mid-size SaaS provider runs dozens of microservices across Kubernetes and multi-cloud environments.
- They had observability, but it was store-first, analyze-later.
- Their AI incident assistant was producing false positives and alert storms, eroding trust with SREs.
- Costs were ballooning: log volumes doubled year over year, driven by debug logs and high-cardinality metrics.
The “Before” State: Drowning in Noise
Telemetry Characteristics
- Logs: 5 TB/day, with ~40% redundant error messages (retry storms, health check chatter).
- Metrics: 50k+ unique time series, mostly due to exploding labels (user_id, session_id).
- Traces: Sampling was uniform, so “healthy” traces outnumbered “error” traces 20:1.
- Events: Deploy markers often missing or inconsistent.
Impact on AI Agents & Humans
- Signal-to-noise ratio: ~1 in 50 alerts led to a true incident.
- MTTR: Median time to resolve was 90 minutes, slowed by sifting irrelevant logs.
- Agent performance: AI assistant raised P1 rollback recommendations that were overruled 70% of the time.
- Costs: Storage + rehydration consumed 45% of the observability budget.
The Intervention: Mezmo Active Telemetry
The team implemented an agent-aware telemetry pipeline in three stages:
a) Shape at Ingest
- Dropped debug logs in prod.
- Deduplicated identical error bursts into fingerprints with counters.
- Added tail-based sampling for traces (kept 100% anomalous, dropped 90% healthy).
b) Enrich & Govern
- Enriched logs with service owner, deploy SHA, tenant tier.
- Tagged every record with policy_version + provenance metadata.
- Applied PII detection and redaction inline (emails, tokens).
c) Route by Intent
- Labeled signals as incident_candidate, security_event, or usage_signal.
- Routed incident candidates to Live Tail and the AI assistant within seconds.
- Stored raw verbose data in cold archive, but only context-ready slices went to agents.
The “After” State: Clear Signal, Lower Cost
Telemetry Outcomes
- Logs: Reduced from 5 TB/day → 2.8 TB/day (44% reduction).
- Metrics: Cardinality capped, unique series down 35%.
- Traces: Error traces now 1 in 3 sampled records instead of 1 in 20.
- Events: Deploy metadata now attached automatically at ingest.
Impact on AI Agents & Humans
- Signal-to-noise ratio: Improved from 1:50 → 1:7 (true incidents surfaced faster).
- MTTR: Dropped from 90 min → 55 min, thanks to Live Tail enriched signals.
- Agent performance: 80% of rollback recommendations approved (vs. 30% before).
- Costs: Storage spend cut by ~30%, fewer costly rehydrations required.
Before vs. After Snapshot
Lessons Learned
- Noise reduction is not optional: Deduplication and sampling sharpened both human and AI visibility.
- Context beats volume: Attaching deploy SHA + ownership metadata turned vague errors into actionable insights.
- Governance builds trust: PII redaction + transparent lineage made it safe to let agents consume telemetry directly.
- Live Tail = faster feedback: Humans and agents benefited from near-instant enriched signals.
By shifting from a store-first model to an agent-aware telemetry pipeline, the SaaS provider turned a noisy, expensive observability practice into a lean, compliant, and agent-ready system - improving MTTR, cutting costs, and boosting trust in AI-driven operations.
Routing savings and faster remediation
A global fintech company with strict compliance requirements and a sprawling Kubernetes estate was struggling with observability sprawl:
- Every log, metric, trace, and event was routed to all sinks (SIEM, search, data lake, analytics, AI incident assistant).
- This “spray-and-pray” approach doubled ingestion costs yearly.
- Engineers faced alert storms and struggled to isolate true incidents quickly.
Key pain points:
- Costs: ~60% of observability budget spent on duplicative routing and storage.
- MTTR: 2+ hours median resolution time.
- Agent performance: AI assistant slowed by unnecessary data, drowning in low-value signals.
The “Before” State: Everything Everywhere All at Once
- Logs: 7 TB/day, ingested across 4 systems.
- Traces: Sampled uniformly, but sent to both analytics and search (double cost).
- Security telemetry: PCI data stored in multiple downstreams, requiring duplicate compliance checks.
- Agent feed: Agents received raw streams with noise, causing false positives in remediation suggestions.
Result:
- Overpaying for duplicated data.
- Delayed remediation since engineers and agents had to wade through irrelevant telemetry.
- Audit risks from uncontrolled sensitive data replication.
The Intervention: Agent-Aware Routing with Mezmo
The team adopted Mezmo Active Telemetry to route by intent and context, not by default duplication.
a) Routing by Intent
- incident_candidate signals → routed to Live Tail + AI agent.
- security_event → routed only to SIEM + audit store.
- usage_signal → routed to analytics/lake only.
- low-value debug → dropped at ingest or cold-archived.
b) Context-Ready Enrichment
- Each record tagged with: service owner, deploy SHA, SLA tier.
- Policies applied inline: PII detection, masking, residency enforcement.
- Audit lineage tracked which sinks each record touched.
c) Smart Sinks
- Hot store (14d retention) for incidents and search.
- Warm lake (90d) for compliance and usage analytics.
- Cold archive (>90d) for regulatory hold.
- AI agent sink receives lean, enriched streams — not raw volume.
The “After” State: Lean Routing, Faster Fixes
Routing Savings:
- Logs: 7 TB/day → 3.9 TB/day delivered (44% reduction).
- Duplication eliminated: telemetry routed once, not 4x.
- Security telemetry: PCI/PHI restricted to SIEM only, cutting redundant storage by 65%.
- Overall pipeline cost down 35%.
Faster Remediation:
- Incident candidates enriched with ownership + deploy info.
- AI agents now propose actions with higher precision (rollback approvals up from 40% → 82%).
- MTTR dropped from 2h10m → 1h5m (51% faster).
- Oncall reduced alert review load by 60%.
Before vs. After Snapshot
Lessons Learned
- Routing by intent prevents duplication and makes telemetry spend predictable.
- Context enrichment ensures that reduced data volume still carries the necessary signal.
- Governance inline avoids compliance risks by stopping PII spread before it hits multiple sinks.
- Agents thrive on less but better data and precision and trust increase dramatically.
By shifting to agent-aware routing, the fintech cut routing and storage costs by over a third, tightened compliance, and halved MTTR. The AI assistant became more trusted and effective because it was fed only context-rich, intent-labeled telemetry.
How agents improved with better context
A cloud-based e-commerce platform had invested in AI assistants to accelerate incident response.
- The agents ingested all logs, metrics, and traces, including raw and unfiltered.
- Engineers hoped the agents would surface anomalies and suggest remediations faster.
- Instead, the agents were overwhelmed by noise and lacked critical context markers (e.g., which team owned a service, which deploy just happened).
Key challenges:
- High false positives: Agents opened P1 tickets on minor errors.
- Blind spots: Agents missed obvious deploy-linked failures because deploy metadata wasn’t attached to telemetry.
- Low trust: Only ~35% of agent recommendations were accepted by SREs.
The “Before” State: Volume Without Context
- Logs: 4.5 TB/day, including retries, debug chatter, and repetitive error bursts.
- Metrics: High cardinality (per user/session labels) with little normalization.
- Traces: Sampled uniformly, mostly healthy paths.
- Agent output:
- Suggested remediations with weak rationale (“service error spike detected”).
- 65% of recommendations rejected due to lack of evidence or context.
- Agents treated every error spike as a P1, regardless of customer impact.
Outcome: Agents were seen as “noisy interns,” not trusted copilots.
The Intervention: Enriching Context in the Pipeline
The team implemented Mezmo Active Telemetry with a focus on context enrichment:
a) Data Shaping at Ingest
- Deduplication collapsed repetitive error floods.
- Intent-aware sampling ensured error traces were prioritized over healthy ones.
b) Context Enrichment
- Every signal tagged with:
- Service owner + oncall team
- Deploy SHA + feature flag
- Tenant tier (SLA level)
- Runbook URL
- PII masking applied inline, ensuring safe agent input.
c) Routing by Intent
- incident_candidate signals routed in real time to agents with full enrichment.
- Usage and debug signals sent only to analytics/lake (not agents).
- Security-sensitive telemetry routed only to SIEM.
The “After” State: Smarter, Trusted Agents
Agent Improvements:
- Accuracy: True positive rate rose sharply; 78% of agent recommendations accepted (vs. 35% before).
- Relevance: Agents correlated deploy metadata with error spikes → correctly identified rollback candidates.
- Clarity: Recommendations now included structured rationale (“Errors increased 320% after deploy SHA 7d23a, impacting Tier 1 tenants; rollback recommended”).
- Trust: SREs shifted from rejecting most recommendations to using agents as first responders.
Operational Gains:
- MTTR: Improved from 85 min → 50 min (41% faster).
- Alert fatigue: Oncall alert volume dropped by ~55%.
- Cost: Telemetry volume to agents cut in half, reducing token processing costs.
Before vs. After Snapshot
Lessons Learned
- Volume ≠ intelligence: Raw data made agents noisy, not useful.
- Context unlocks insight: Deploy info, tenant tier, and ownership metadata transformed vague “symptoms” into actionable diagnoses.
- Governance builds safety: Masking and policy tagging ensured agents could consume telemetry without compliance risks.
- Human trust follows clarity: When agents explained why they recommended an action, humans started approving them.
By enriching telemetry with ownership, deploy, tenant, and compliance context, the e-commerce platform turned agents from noisy interns into trusted copilots. The result was higher accuracy, faster remediation, reduced costs, and a stronger partnership between humans and AI in incident response.
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