Context Engineering for Observability: How to Deliver the Right Data to LLMs

What Is Context Engineering?

Context Engineering is the emerging discipline of designing, managing, and optimizing the context that large language models (LLMs) and AI agents use to generate accurate, useful, and safe outputs. Where traditional software engineering is about building deterministic systems, context engineering is about shaping the inputs, prompts, memory, knowledge sources, and environmental conditions that guide probabilistic AI models. Context Engineering is to AI systems what software engineering is to traditional apps: the structured discipline that makes them reliable, safe, and practical.

LLMs don’t “know” in the human sense: they generate responses based on the context window provided (the prompt + retrieved documents + system instructions). Context engineering is the structured practice of controlling this environment to maximize reliability, relevance, and efficiency.

Context engineering has a number of key components:

Prompt Design and Structuring

• Crafting precise, layered prompts (system, user, assistant instructions).
• Using roles, constraints, and examples to guide outputs.

Context Management

• Selecting what information goes into the LLM’s context window.
• Prioritizing relevant facts, trimming noise, and shaping responses.

Retrieval-Augmented Generation (RAG)

• Dynamically pulling in external knowledge (docs, APIs, databases).
• Engineering embeddings, retrieval strategies, and ranking for accuracy.

Memory and State Handling

• Deciding how much history or prior interaction to persist.
• Balancing short-term vs long-term memory for agents.

Optimization for Efficiency

• Minimizing token usage (cost, latency).
• Filtering redundant or irrelevant details.

Safety and Guardrails

• Embedding rules, policies, or constraints directly in context.
• Ensuring compliance with domain-specific or regulatory needs.

The Importance of Context Engineering

Context engineering is important because it determines how useful, reliable, efficient, and safe AI systems are in real-world use. Without it, large language models tend to hallucinate, overrun costs, or fail to deliver consistent results. Context engineering helps provide accuracy, scalability, reliability, and safety.

LLMs generate outputs based on the context window they see. If the wrong or incomplete context is provided, the model may hallucinate missing facts, responses may drift off-topic, and critical details can be overlooked. Also, context windows are expensive in both compute and money (tokens), and feeding too much irrelevant or redundant text increases cost and latency. Context engineering helps trim noise, compress information, and prioritize high-value inputs — delivering faster, cheaper results. Context can embed rules, guardrails, and compliance constraints directly into AI behavior so organizations can trust AI systems to operate within safe, legal, and ethical boundaries. Unstructured prompting leads to inconsistent outputs, but context engineering standardizes how inputs, prompts, and retrievals are structured — leading to repeatable, predictable outcomes. Finally, as AI systems grow more complex (multi-agent setups, RAG pipelines, domain-specific apps), the importance of managing context across sources increases. Without engineered context, scaling AI leads to chaos, cost bloat, and governance risks.

Context engineering is important because it turns raw AI potential into practical, accurate, cost-efficient, and safe systems. It’s the foundation that transforms LLMs from “impressive demos” into enterprise-ready, mission-critical tools.

How It Powers AI Agents

AI agents live and die by the quality of their context. Context engineering is what makes them intelligent, adaptive, and safe enough to operate in complex environments instead of just responding like a static chatbot.

Agents need clear instructions, goals, and constraints to operate effectively. Context engineering frames the role, objective, and rules of engagement for the agent. Without this engineered context, the agent may misinterpret tasks or act inconsistently.

Agents don’t “know” everything; they rely on retrieval-augmented generation and memory systems. Context engineering ensures they fetch the right information and store/reuse relevant history. This prevents hallucinations and makes responses accurate, fast, and cost-effective.

In agentic systems, multiple agents may need to share state, plans, or partial outputs. Context engineering defines how agents communicate, what data gets passed along, and what doesn’t (to avoid overload). This keeps communication efficient and prevents context windows from exploding.

Agents often run in loops or chains of reasoning. Context engineering trims unnecessary history, compresses state, and prioritizes high-value context. This reduces cost, speeds up responses, and avoids “lost in the weeds” reasoning.

Context carries rules, constraints, and compliance requirements. Agents use this as their operating boundary to avoid unsafe, biased, or non-compliant actions. Guardrails baked into context keep agents safe and trustworthy.

Context engineering lets agents adapt to changing environments by injecting dynamic context (e.g., live telemetry, user preferences, environmental data). This gives agents situational awareness and responsiveness.

Context engineering powers AI agents by giving them clear goals, relevant knowledge, efficient communication, safe boundaries, and adaptive awareness. Without it, agents are just LLMs with no discipline; with it, they become reliable collaborators that can handle complex, multi-step, real-world tasks.

Context Engineering vs. Prompt Engineering

These two terms are often confused, but they operate at different levels of abstraction. Think of Prompt Engineering as the tactical art of crafting a single instruction, while Context Engineering is the strategic discipline of designing the whole environment an AI system operates in.

Prompt Engineering:

• Focuses on one-off instructions or templates.
• Example: “Summarize this text in three bullet points.”

Context Engineering:

• Governs the entire information ecosystem feeding into the model: prompts, retrieved docs, memory, policies, and constraints.
• Example: Designing a pipeline where the model always sees:
  
  1. System role definition
  2. Latest customer data from a CRM
  3. Relevant product FAQs
  4. Safety guardrails

To think about it slightly differently, prompt engineering writes the recipe, while context engineering stocks the kitchen, chooses the ingredients and sets the rules for cooking.

From Static Prompts to Dynamic Systems

Static prompts to dynamic systems is the natural evolution from prompt engineering to context engineering. 

Early LLM use relied on manually crafted prompts. Developers spent time fine-tuning phrasing (“act as…”, “answer step by step…”, “summarize in bullets”). These prompts were static: fixed templates that didn’t change much between queries.

As use cases matured, prompts alone weren’t enough. AI systems needed real-time context, dynamic retrieval, memory, and guardrails. Instead of one static string, the model is fed a structured, evolving context pipeline.

This shift is important for a number of reasons including:

• Accuracy: Dynamic retrieval ensures outputs are grounded in the right data.
• Efficiency: Context shaping reduces token waste vs. stuffing everything in.
• Safety: Guardrails can’t be reliably enforced with static prompts alone — but engineered context enforces them.
• Agents: Multi-agent AI requires context sharing and adaptive inputs, impossible with static prompts.
• Scalability: Static prompts break when applied across 100s of workflows; dynamic systems scale.

Or to put it another way: Prompt Engineering was about “what words to type.” Context Engineering is about “how to design systems that continuously deliver the right information, rules, and memory into the model.”

When Prompting Still Matters

Even though context engineering is the broader, more powerful discipline, prompting (engineering) still matters a lot. Think of it like this: context engineering sets the stage, but prompt engineering directs the performance.

Prompt engineering is still critical for small, well-defined tasks, shaping model behavior, teaching by example, reinforcing local constraints or guardrails, multi-agent role definition, and debugging and iteration.

Key Elements of Context in AI Systems

The “context” in AI systems is everything the model sees and uses to generate an answer. Context isn’t just the prompt — it’s the entire engineered environment around the model.

Here are the key elements of context in AI systems:

Instructions and System Prompts

System Instructions (Role and Rules)

• High-level directives that define who the AI is and how it should behave.
• Usually invisible to the end-user (e.g., “You are a helpful assistant that answers in JSON when asked about data”).

Why it matters: Provides stability, consistency, and enforces high-level policies.

User Input and History

User Input (Prompt / Query)

• The immediate request or question from the user.
• Could be a single instruction (“summarize this report”) or part of a longer dialogue.

Why it matters: Directs the model’s focus toward the task at hand.

Retrieved Knowledge and External Data

Retrieved Knowledge (External Data Sources)

• Information dynamically pulled in via Retrieval-Augmented Generation (RAG).
• Could be docs, databases, APIs, telemetry logs, CRM records, etc.

Why it matters: Grounds outputs in factual, up-to-date knowledge instead of relying on the model’s training data alone.

Output Formatting

Output Formatting and Style Guidance

• Instructions on how answers should look.
• Examples:
  
  • “Answer in markdown.”
  • “Return JSON with these fields.”
  • “Use a professional but friendly tone.”

Why it matters: Turns raw reasoning into usable, consistent outputs.

The key elements of context in AI systems are:

1. System instructions (role/rules)
2. User input (prompt/query)
3. Retrieved knowledge (external data)
4. Memory (short + long term)
5. Structured context (metadata, environment, state)
6. Policies & guardrails (safety/compliance)
7. Output formatting & style guidance

Together, these elements move an AI from just answering a prompt to operating inside a dynamic, engineered system that’s accurate, efficient, and safe.

Tool Access and Function Definitions

Tool access and function definitions are indeed additional, often overlooked, key elements of context in AI systems. They matter a lot when models go beyond just text and start acting in the world. Let’s break them down:

Tool access defines which external capabilities the AI is allowed to use and how to use them. Tool access extends model capability, provides context shaping and governance and safety. Function definitions tell the AI how to use a tool or API - the contract. Function definitions give teams disambiguation, reliability, interpretability, and safety. Together, they become part of the engineered context that enables an AI agent not just to reason, but also to act on the world in controlled, reliable ways.

Why Context Engineering Matters for Observability and Reliability

Context engineering is critical in observability and reliability because telemetry data (logs, metrics, traces, alerts) is noisy, high-volume, and constantly changing. Without engineered context, AI-powered observability systems (and humans using them) drown in irrelevant data.

Here are the key areas where context engineering is a game changer in observability and reliability.

Signal over noise:

• Observability data is notoriously high-cardinality and verbose.
• Raw logs or metrics often overwhelm incident responders with irrelevant detail.
• Context engineering ensures only the most relevant signals (severity, impacted service, error pattern) enter the AI or monitoring system’s “attention window.”

Root cause analysis:

• During outages, engineers don’t just want data — they want answers.
• Context engineering shapes telemetry into causal chains:
  
  • Error logs → correlated metrics → upstream/downstream traces.
• The AI agent or SRE then sees a coherent context instead of fragmented events.

Dynamic context for incidents:

• In reliability workflows, context isn’t static — it evolves as new signals arrive.
• Context engineering dynamically refreshes what’s “in scope” for the AI/system:
  
  • Before incident: baseline logs & metrics.
  • During incident: anomaly signals + relevant traces.
  • Post-incident: enriched timeline for review.

Cost optimization:

• Observability pipelines can explode in cost when all data is ingested blindly.
• Context engineering powers data shaping, sampling, and prioritization before ingestion.
• Instead of storing everything, systems retain what’s relevant for reliability and compliance.

Multi-agent coordination:

• Modern observability often involves AI agents:
  
  • Log summarizer agent.
  • Metrics anomaly detector agent.
  • Incident triage agent.
• Context engineering controls what data each agent sees, and how they hand off findings.

Guardrails and compliance:

• Observability data often contains sensitive information (e.g., PII in logs).
• Context engineering enforces guardrails:
  
  • Redacts sensitive fields before they enter the model’s context.
  • Applies role-based visibility (what an on-call engineer vs. a customer-support agent can see).

Without context engineering, observability is just “data hoarding.” With it, observability becomes actionable intelligence that drives reliability.

Context Failures vs. Model Failures

The distinction between context failures and model failures is essential when thinking about why AI systems succeed or fail. Many teams blame the model when in fact the real issue is the context. 

Context failure means the model is fine, but it was given poor, missing, or noisy context, so its output is off. Causes could include incomplete or irrelevant data in the context window, retrieval pipeline returns wrong documents, context window overflow (important details get truncated), conflicting or ambiguous instructions, or sensitive info not scrubbed, causing compliance/safety issues.

The problem wasn’t the model — it was missing the right telemetry context.

Model failures means the model itself fails due to limitations in its reasoning, training data, or architecture — even with correct context. Causes could include the model lacks domain knowledge (not in pretraining), poor reasoning or math abilities, bias in training data, output instability (same input yields wildly different outputs), or inability to follow complex multi-step logic.

Context failures can be fixed with better context engineering: improve retrieval, add memory/state management, prioritize relevant logs/metrics, and or shape input with guardrails. Many “model problems” in enterprise AI are actually context problems.

Telemetry Data and Agent Behavior

Context engineering is the bridge that makes telemetry data and agent behavior work together in observability and reliability. Without context engineering: telemetry overwhelms agents. With context engineering: telemetry empowers agents to improve observability and reliability.

Telemetry generates firehoses of logs, metrics, and traces. Without context engineering, AI agents are overwhelmed by noise, making it impossible to reason about incidents or reliability. Agents don’t see everything — they see the engineered slice of telemetry most relevant to the task. Agent “intelligence” isn’t magic — it’s context-driven. Telemetry data is the “raw material” and agent behavior is the “response mechanism.” Context engineering is the refinery that turns noisy telemetry into actionable signal, enabling agents to behave intelligently and reliably.

Context Engineering Best Practices

Experts have a number of best practices to share including:

• Define roles and instructions clearly.
• Prioritize relevance over volume.
• Engineer retrieval pipelines (RAG).
• Manage memory strategically.
• Enforce guardrails and compliance in context.
• Optimize for cost and efficiency.
• Standardize output formats.
• Design for multi-agent coordination.

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Trace and evaluate with observability tools

Treat context engineering like software: test prompts, retrieval accuracy, and outputs. Monitor failure cases → determine if they’re context failures or model failures. Adjust filters, instructions, or retrieval ranking iteratively.

Automate context assembly

Create reusable context templates for common scenarios (incident triage, log summarization, anomaly detection). Document what goes into system, user, and tool contexts. Share best practices across teams to ensure consistency.

Design for flexibility and control

To achieve flexibility and maintain control, use modular context pipelines, apply context scoping rules, enable dynamic retrieval with ranking, and summarize and compress strategically.

Structure inputs for LLM understanding

To ensure LLM understanding use clear sectioning and labels, provide metadata alongside data, normalize formats across inputs, and summarize before injecting raw data. behavior.

Context engineering examples

Example 1: Customer Support Bot with RAG

The goal is a bot that can answer customer support questions accurately, efficiently, and safely, using company documentation and user account data, without hallucination or policy violations.

System Instructions (Role & Rules)

Retrieved Knowledge (RAG Pipeline)

• Bot queries knowledge base + CRM for relevant info.
• Returns:
  
  • FAQ doc snippet: “Payments may fail due to expired cards, insufficient funds, or fraud flags.”
  • CRM account data: “Last payment declined on 2025-08-20 due to expired card ending in 1234.”

Context Assembly (Structured Input)

The context window sent to the LLM might look like:

Expected Agent Behavior

The LLM generates:

This example shows how context engineering + RAG transforms a static prompt (“Why was my payment declined?”) into a dynamic, safe, and reliable customer support workflow.

Example 2: Pipeline Tuning for Real-Time Systems

Here’s how to tune a pipeline to enable an incident triage agent to analyze telemetry streams in near real-time, without being overwhelmed by raw data, while ensuring low latency, cost efficiency, and reliable outputs.

Pre-Processing layer (noise reduction)

• Apply filters and sampling before data hits the model.
• Example:
  
  • Keep error and warning logs.
  • Drop or batch debug/info logs unless explicitly requested.
  • Use sliding windows for metrics (1-min, 5-min averages).

Aggregation and compression

• Summarize repeated events.
• Example: Instead of 50k identical “HTTP 503 errors,” compress to:
  
  • “❌ 50,000 payment API 503 errors in last 2 minutes.”

Aggregate metrics into structured summaries:

Dynamic retrieval (RAG for Telemetry)

• Retrieve only relevant traces/logs correlated to the incident.
• Example: If checkout service latency spikes, fetch:
  
  • Related traces in payment API.
  • Logs tagged with the same trace ID.

Inject dynamically into context.

Context prioritization and ordering

• Order context by importance:
  
  1. Incident task instructions + role definition.
  2. Guardrails (compliance, formatting).
  3. Critical metrics (spikes, errors).
  4. Aggregated log/traces.
  5. Supporting historical context (last incident timeline).

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Feedback and control loops

• Pipeline adjusts based on agent queries or user needs:
  
  • If RCA agent asks for “root cause candidates,” pipeline fetches deeper traces.
  • If only triage is needed, pipeline limits detail to anomalies only.

Context assembly example

### Task  

You are an incident triage agent. Identify the likely cause of the current reliability issue.  

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### Guardrails  

- Never output raw PII or customer data.  

- Summarize findings in JSON with keys: RootCause, Impact, RecommendedAction.  

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### Key Metrics (Checkout Service, 16:00–16:05 UTC)  

- Latency P95: 2.3s (baseline: 400ms)  

- Error Rate: 18% (baseline: <1%)  

- CPU Utilization: 85%  

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### Aggregated Logs  

❌ 50,000 payment API 503 errors in last 2 minutes.  

⚠️ Timeout errors detected between Checkout → Payment API.  

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### Correlated Trace Sample  

TraceID=abc123 → Checkout request failed due to Payment API timeout.  

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### Prior Incident History (Last 7 Days)  

- Aug 20: Similar issue caused by expired TLS cert on Payment API. 

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Expected Agent Behavior

Agent outputs:

{

  "RootCause": "Payment API timeouts causing checkout service errors.",

  "Impact": "High latency and failed transactions for end-users.",

  "RecommendedAction": "Escalate to Payment API team; check for service overload or expired cert."

}

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Pipeline tuning via context engineering turns raw telemetry streams into actionable, real-time reliability insights.

How Mezmo Supports Context Engineering at Scale

Mezmo (a telemetry and observability platform) is actually a natural enabler of context engineering at scale. The way Mezmo handles log/metric/trace pipelines, enrichment, and shaping directly aligns with the core best practices of context engineering. 

Real-Time data filtering and shaping

Mezmo lets teams filter, transform, and enrich telemetry data before it ever hits storage or an AI system. Context engineering requires that only relevant, structured signals are fed into models - Mezmo’s pipelines do exactly this by filtering out noise, sampling at scale to reduce cardinality, and enriching logs with metadata.

Delivering the right context at the right time

Mezmo’s pipeline architecture allows for real-time routing and transformation of observability data. AI systems can consume just-in-time context for different tasks. And Mezmo enables prioritizing which signals matter most for ingestion and analysis. By trimming redundant data and routing only critical events into context, Mezmo optimizes token usage (for lower LLM costs), storage overhead, and response latency.

Also, Mezo offers built-in guardrails and compliance, multi-agent and multi-tool integration, and scalable real-time context for reliability.

Mezmo is not just an observability tool - it’s a context engineering engine, turning raw telemetry into structured, safe, and actionable intelligence for AI-driven reliability.

Conclusion: Context Is the New Interface

In the age of AI, context has become the new interface. Instead of clicking through dashboards or writing code, we guide systems by designing the information, rules, and signals they see. Well-engineered context determines whether AI is noisy or insightful, costly or efficient, risky or reliable. By shaping what goes into the model, we shape what comes out - making context engineering the foundation for usable, scalable, and trustworthy AI.

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