Overview
- Braintrust, Arize AX, Galileo, Langfuse, LangSmith, Datadog LLM Observability, and Confident AI help you evaluate, trace, and monitor agent behavior.
- These platforms answer whether an agent behaved correctly, but their core observability products do not investigate and remediate broader production incidents.
- Mezmo AURA sits above your eval stack as an autonomous execution harness and first responder for production incidents.
- AURA uses open-source components and your chosen models, infrastructure, and tools to understand incidents, act on them, and improve future response.
- If you need evals and tracing, the comparison table may provide enough guidance. Keep reading if live agents already enter your incident path and require production response.
When the eval suite is green and production is still broken
A passing eval suite cannot predict every production failure. An agent might call the correct tool at turn three, misread its response, and continue reasoning from bad data until turn eight. The final model call can look normal even though the full session produced the wrong customer outcome.
Agent observability tools reconstruct those multi-turn paths. Traditional application monitoring might record a successful API response, while a causal trace reveals that the agent misinterpreted the returned data and made several bad decisions afterward. Individual model calls often hide agent failures because agents maintain state, use tools, and pursue goals across many steps.
Production behavior also creates failure modes that curated tests never covered. As one developer guide puts it, production failures keep outrunning the eval set. Real users provide unfamiliar inputs, connected systems return stale data, and workflows encounter states absent from pre-release datasets. A green evaluation therefore shows that an agent passed known tests. It cannot guarantee that the surrounding production system will operate correctly.
Buyers eventually face two different questions. Is the agent behaving correctly across traces, prompts, tool calls, and test cases? If the agent has already contributed to a live incident, who investigates the wider system and executes a safe response?
The comparison table and seven vendor reviews address the first question. They examine strong platforms for evaluation, tracing, monitoring, and runtime guardrails. Teams that need those capabilities can choose among them. The closing AURA section addresses the second question by examining production investigation and remediation after an agent-driven system breaks.
Comparing the 7 eval and tracing platforms plus Mezmo/AURA
The comparison separates tools that evaluate or trace agent behavior from software that investigates and remediates production incidents. Most entries help you detect failures, inspect traces, or block unsafe outputs. AURA serves as an autonomous execution harness and first responder after an incident occurs.
The vendor sections below explain each platform’s architecture and strengths. The final column shows where observability ends and production incident response begins.
Braintrust
Braintrust fits teams that want evaluation, trace analysis, and prompt experimentation in one platform. Engineers can convert production traces into datasets, compare prompt or model variants, and inspect regressions before release. Brainstore supports fast searches across large trace volumes, while Loop generates test cases, runs evaluations, and iterates on prompts when you provide clear scoring criteria. A third-party product review describes these connected workflows as Braintrust’s core strength.
Braintrust uses a downstream, post-inference architecture. Its SDK and optional Gateway capture model calls, outputs, latency, cost, and metadata after the model runs. Braintrust can report a runaway agent loop or failed tool call, but it cannot enforce token budgets, approve tool use, or stop unsafe execution in real time.
Braintrust works best for prompt optimization, release testing, and ongoing output-quality analysis. The MIT-licensed SDKs offer some inspectability, but the core platform remains closed source, and self-hosting is limited to the Enterprise tier. Teams that need LLM evals may find Braintrust sufficient. Teams managing incidents caused or complicated by live agents need a separate response layer that can investigate the wider production environment and execute remediation.
Arize AX
Arize AX suits teams that need continuous evaluation and monitoring across high-volume production LLM workloads. It builds on Phoenix, Arize’s self-managed product for tracing and evaluation. Phoenix uses OpenTelemetry and OpenInference instrumentation, while the managed AX layer adds production monitoring and governance for larger deployments (Arize).
Signal reviews production traces on a schedule and groups related failures into prioritized issues with supporting evidence. Alyx investigates trace-level problems and proposes evaluators that could detect similar behavior. A person must confirm those evaluators before Arize applies them, which keeps evaluation changes under human control.
Arize AX stops at investigation, evaluation, and notification. Its monitors can route alerts to PagerDuty or Slack, but Alyx does not remediate the affected service or coordinate response across broader infrastructure. Arize AX therefore fits teams that need production-scale LLM observability and stronger evaluation coverage. Teams seeking automated incident diagnosis and remediation will need an execution layer above it.
Galileo
Galileo turns evaluation criteria into runtime guardrails for agent behavior. Its evaluation tools assess retrieval quality, instruction adherence, tool use, safety, and other aspects of an agent trace. Proprietary Luna-2 models run those checks at lower cost than larger judge models, which makes continuous scoring practical across production traffic.
Runtime Protection uses evaluation scores during inference. Input guardrails screen prompts for attacks or malformed requests, while output guardrails filter hallucinations, policy violations, and sensitive data. Action policies can also stop unauthorized tool calls and preserve decision rationales for audits.
Galileo fits teams that need evaluations and active enforcement within the same platform. Trace and graph views help you inspect agent decisions, while its deployment options include SaaS, private cloud, and on-premises environments.
Runtime Protection focuses on unsafe model outputs and agent actions. Galileo does not describe automated root-cause investigation across application and infrastructure telemetry, nor does it execute broader SRE remediation workflows. A blocked tool call may contain immediate harm, but Galileo does not diagnose the service failure that caused an agent-driven production incident or restore the affected system.
Langfuse
Langfuse gives technical teams an open-source platform for tracing LLM applications and managing prompts. Its documented feature set captures model calls, retrieval steps, and agent actions. Evaluations support model-based judging, code evaluators, user feedback, and custom pipelines.
Self-hosting gives you direct control over deployment and application data. Langfuse uses an MIT license for its core repository, while some enterprise features carry a commercial license. You can deploy it through Docker Compose, Kubernetes, or cloud infrastructure templates. Langfuse Cloud provides a managed alternative for teams that do not want to operate the platform themselves.
Langfuse works best when developers need to inspect traces, test prompts, and evaluate changes before or after release. A developer can open a poor trace in the playground, adjust the prompt or model settings, and compare the new output against a dataset.
Langfuse does not provide a documented workflow for production incident response. Its application-focused scope does not include on-call coordination, infrastructure-level root-cause investigation, or automated remediation. You can use Langfuse to determine how an agent behaved, but you need a separate execution layer to investigate and resolve incidents across the production environment.
LangSmith
LangSmith provides debugging, testing, evaluation, and monitoring for LLM applications. Its traces expose model calls, tool use, intermediate agent steps, latency, and errors. You can also run dataset-based tests and automated evaluators to compare prompts or application versions before release.
LangSmith fits teams building primarily with LangChain or LangGraph. Its instrumentation can accept traces from custom code, including OpenTelemetry spans, but its debugging model follows LangChain concepts such as chains and agent steps. Applications that mix other orchestration frameworks or direct model calls may require extra instrumentation and provide less consistent trace detail.
LangSmith helps you determine where an agent behaved incorrectly and alert an operator when production metrics cross a threshold. Its monitoring does not investigate infrastructure-wide incidents or execute remediation. If a faulty agent action affects a database, service, or deployment, LangSmith can provide relevant trace evidence, but your incident-response tools and on-call staff must diagnose and fix the broader failure.
Datadog LLM Observability
Datadog LLM Observability suits teams that already use Datadog for application and infrastructure monitoring. The module correlates LLM traces with APM, infrastructure, and RUM data, which helps engineers connect a poor agent response to service errors, latency, or resource constraints elsewhere in the stack. Datadog delivers the module as multi-tenant SaaS, so teams seeking self-hosted or open-source workflows may prefer another option.
Datadog instruments LLM applications through an SDK that records prompts, responses, tool activity, latency, and token usage. The SDK sends trace data after or alongside each call rather than sitting between the application and model. As a result, most evaluations detect problems after the model has responded. AI Guard provides a narrower exception by blocking security threats such as prompt injection and unauthorized tool calls during runtime.
Bits AI SRE serves a separate purpose within Datadog. LLM Observability monitors applications that you build, while Bits AI SRE investigates alerts and proposes likely root causes across Datadog telemetry. The LLM Observability module can surface quality, cost, and performance failures, but it does not execute general remediation for production incidents.
Confident AI / DeepEval
Confident AI pairs a managed enterprise platform with DeepEval, its Apache 2.0 framework for testing LLM applications and agents. DeepEval uses familiar Pytest patterns, so you can define expected behavior, run evaluations in CI/CD, and block regressions before deployment. Tests can score complete agent trajectories or individual steps such as tool calls and retrieval.
Confident AI adds shared datasets, experiment tracking, prompt versioning, human annotation, and production trace monitoring. The managed platform fits AI engineering groups that want a consistent evaluation standard across products while keeping tests close to their existing Python and CI workflows.
DeepEval keeps its telemetry scope narrow. Its OpenTelemetry collection records the names of evaluation metrics, not prompts, inputs, outputs, or evaluation data. Confident AI can monitor live traces with online evaluations, but its published capabilities do not describe paging, on-call coordination, incident investigation across the wider production stack, or automated remediation. Choose Confident AI when you need code-native quality testing and regression control. Add a separate incident-response layer when an agent failure affects production services and requires diagnosis or action.
Where these tools stop and production incident response begins
The seven platforms concentrate on detecting, tracing, and evaluating agent behavior. They help you find weak outputs, inspect tool calls, compare prompts, and alert on failures. Some can block unsafe actions, but their primary workflows stop before broader incident investigation and remediation.
Adoption data reflects that emphasis. A survey of more than 1,300 professionals found that 89% had implemented observability, while 52.4% ran offline evaluations and 37.3% ran online evaluations. Another 29.5% reported no evaluation. Those figures do not measure remediation directly, but they show that much of the industry still focuses on establishing detection and evaluation practices.
Production incident response starts after an alert arrives. You must connect an agent failure to its surrounding infrastructure and data, determine the cause, and execute an approved fix. An alert routed to Slack or PagerDuty transfers that work to a person.
Teams that need evaluations and traces should use the tools compared above. Teams whose agents can trigger production incidents need an execution layer that investigates failures, takes corrective action, and improves future response workflows.
Mezmo AURA: the layer that acts once agents are live in production
AURA’s Apache 2.0 code and MCP-native architecture let it work with existing evaluation, observability, and remediation tools. You can connect your preferred model, expose tools through MCP, and run AURA on infrastructure you control. Its framework-independent design lets it sit above any of the seven platforms in this comparison without replacing them.
AURA is an SRE Agent for Production, and Mezmo built it to help SREs manage production environments. It acts as an autonomous execution harness and first responder when an agent-driven system causes or encounters an incident.
Understand
AURA investigates incidents by gathering telemetry and operational context through connected MCP tools. Its reasoning loop plans an investigation, executes tool calls, synthesizes the evidence, and checks whether the evidence supports its diagnosis. If the evidence remains incomplete, AURA can revise the plan and continue investigating.
Engineers can inspect which signals AURA used and why it favored one hypothesis over another. That visibility helps you review a diagnosis before approving any production action.
Act
AURA turns a diagnosis into a proposed response through the tools and runbooks you connect. You decide which actions AURA can attempt and which changes require review.
Human approval gates control the path toward greater autonomy. You can require approval before every action, permit routine actions with later review, or let AURA handle established cases while escalating unfamiliar ones. Version-controlled configuration records those boundaries so reviewers can see what the agent may read and execute.
Improve
AURA checks whether an approved change resolved the underlying condition before closing the investigation. It then compresses the useful evidence, reasoning, and remediation path into reusable memory for future incidents.
A confirmed fix can inform the next similar diagnosis or become a hardening rule shared through your configuration repository. Each investigation therefore gives AURA reviewed operational context instead of forcing it to rebuild the same understanding from raw telemetry.
AURA does not compete with Braintrust, Arize, Galileo, Langfuse, LangSmith, Datadog LLM Observability, or Confident AI on evals. If you need evaluation and tracing, keep the tool that fits your development workflow. Add AURA when agents are live in production and you need investigation, controlled remediation, and reusable incident knowledge.
Which tool fits your team
Teams focused on pre-launch testing should stay with an eval or tracing platform. Braintrust supports experimentation, Arize AX supports production evaluation, and Galileo adds runtime guardrails. Langfuse fits self-hosted LLM engineering, while LangSmith fits applications built with LangChain or LangGraph. Datadog suits existing Datadog users, and Confident AI fits pytest-style CI workflows.
Agents already in the incident path create a different requirement. Once an agent can change production state, call operational tools, or affect customer workflows, tracing can reveal what happened but cannot investigate and remediate the wider incident. Production failures can also introduce behaviors that the original eval set never tested.
Keep your eval platform when you need regression testing and behavior analysis. Add Mezmo AURA when you need a first responder that investigates and acts on production incidents. AURA provides an open-source execution layer that can connect your existing stack and models without replacing the eval tooling that already works.
FAQs
What is the difference between AI observability and AgentOps?
AI observability measures model quality, latency, cost, and errors. AgentOps extends that scope to agent goals, plans, tool calls, and multi-step traces. Mezmo adds production investigation and action after monitoring detects a problem.
Is LLM observability the same as agent monitoring?
LLM observability examines individual model calls and outputs. Agent monitoring follows state, decisions, and tool use across a complete task. AURA uses that broader operational context to investigate incidents.
Do I need both an evaluation tool and an AI SRE agent?
Evaluation tools test whether an agent produces acceptable behavior under defined conditions. AURA acts as a first responder when live agent-driven systems cause or encounter production incidents. You may need both when agents operate in the production incident path.
Does AURA replace Braintrust, Arize, or LangSmith?
AURA does not replace platforms used for tracing, evaluation, or prompt testing. Mezmo built AURA for production investigation and supervised remediation. You can keep your evaluation platform and add AURA as the execution harness.
Is AURA open source?
AURA is open source under Apache 2.0. You can inspect its reasoning workflows, connect your models and tools, and deploy it on your own infrastructure. Open code reduces orchestration lock-in and supports version-controlled workflows.
