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AI observability shows what an AI system did; AI evaluation judges whether it did the right thing. Observability connects evidence from a request—such as model calls, retrieved context, tool actions, timings, and errors—so a team can reconstruct its execution. Evaluation applies explicit criteria to an output, decision, trace, or conversation. In practice, traces help explain failures, while repeatable evaluations help determine whether a change fixes them.
What AI observability measures
Observability captures and connects operational evidence so a team can answer, “What happened in this request, and where did it go wrong?” A useful trace may include the user input, model and prompt context, retrieved material, tool calls and arguments, intermediate outputs, final response, timings, errors, token use, cost, and available feedback. OpenTelemetry’s Generative AI semantic conventions can help standardize parts of this telemetry.
For an agent, that evidence can reveal whether a poor result began with retrieval, tool selection, an intermediate step, prompt construction, or a later model response. The trace is evidence about execution; it is not, by itself, a judgment that the result was good or bad.
What AI evaluation measures
Evaluation asks whether an output or behavior met defined expectations. A team can score or label a single model response, one decision, an end-to-end trace, or a multi-turn conversation. Criteria might include correctness, quality, task completion, tool choice, safety, or policy adherence. The rubric or metric should fit the task and the failure being investigated.
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OpenAI’s trace-grading documentation describes assigning structured scores or labels to an agent trace—the end-to-end log of decisions, tool calls, and reasoning steps—to assess behavior. It distinguishes evaluating traces across examples from inspecting an individual trace, and describes using trace evaluations to benchmark changes, find regressions, and validate improvements.
Why observability and evaluation are not substitutes
A healthy latency chart or low error rate does not establish that an answer is correct. Conversely, a low evaluation score may identify a bad result without revealing whether retrieval, a tool call, prompt construction, orchestration, or the model caused it. Observability helps diagnose; evaluation makes judgments against criteria repeatable.
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The two can use the same execution data. A production trace makes a run inspectable; an evaluation applied to that trace judges its behavior. For example, a trace may show which documents an agent retrieved and which tool it called, while a task-completion rubric determines whether those actions produced the expected outcome.
Match evaluation scope to the failure
Choose the unit of evaluation based on where the behavior occurs:
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- Single step or run: Assess a narrow decision such as routing, tool choice, or a policy check.
- Trace: Assess a multi-step execution where retrieval, tool use, or state changes combine to produce the result.
- Thread or multi-turn conversation: Assess whether the agent achieves a conversation-level goal and retains relevant context across turns.
A narrow check is easier to interpret when the failure is local. When success depends on several steps or accumulated context, evaluating only the final answer can miss the behavior that caused the outcome.
Choose offline, online, or ad hoc evaluation
| Mode | When to use it | What it helps answer |
|---|---|---|
| Offline | Run evaluations against a fixed dataset before a change ships. | Does this change pass regression checks, benchmarks, or release gates? |
| Online | Score production traces as traffic arrives. | How does deployed behavior perform on criteria such as trajectory, safety, policy adherence, or sentiment? Some criteria do not require a reference answer for every request. |
| Ad hoc | Investigate a pattern found in an observed run or set of runs. | Is this failure important enough to add to online monitoring or a durable offline regression set? |
These modes serve different jobs: a fixed offline dataset supports repeatable pre-release checks, while online evaluation can surface behavior in live traffic. Ad hoc evaluation helps turn an observed pattern into a decision about what should be monitored or tested routinely.
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Turn production failures into a repeatable improvement loop
- Capture a useful trace. Record the request, relevant context, actions, outputs, and system signals needed to reconstruct the execution.
- Investigate the trace. Identify a specific failure mode rather than treating a low score or bad answer as its own explanation.
- Define acceptable behavior. Decide what the system should have done, then preserve the case in a dataset when it is useful. Remove or anonymize sensitive content as needed.
- Fix the cause. The appropriate change may be to a prompt, retrieval path, tool, policy, or application code.
- Check and monitor. Run the case as an offline evaluation before release, then watch production for recurrence. Use human review when judgments are ambiguous and to calibrate automated graders.
What to compare when choosing AI tooling
Product labels overlap: a tool called an observability platform may include evaluation features, and an evaluation workflow may depend on trace capture. Compare capabilities against the work your team needs to do:
- Trace depth: Can you inspect model and tool calls, retrieved context, intermediate state, timings, errors, and feedback?
- Conversation support: Can the system show and evaluate multi-turn thread context?
- Evaluation workflow: Does it support single-run, trace, and thread-level scoring; offline, online, and exploratory evaluation; and datasets or regression checks?
- Human review: Are there rubrics, annotation or review queues, and ways to calibrate automated judgments?
- Instrumentation and correlation: Which frameworks are covered? Is OpenTelemetry supported? Can data be correlated across application, retrieval, model, and infrastructure layers?
- Data handling and governance: Traces can contain sensitive prompts, retrieved documents, or user data. Check retention, access controls, and redaction practices against your team’s requirements.
Implementation details vary. For example, Amazon OpenSearch Service documentation describes hierarchical traces across agent orchestration, model calls, tools, and retrieval operations, using GenAI semantic conventions and OpenTelemetry integration. This is an example of one implementation, not an independent certification or product ranking.
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What survey figures do—and do not—show
LangChain’s AI observability guide and its March 3, 2026 explainer report figures from LangChain’s State of Agent Engineering survey: 89% of organizations had some agent observability, 94% of production-agent teams had some observability, 62% of organizations had detailed tracing, and 72% of production-agent teams had full tracing. The same sources report 52% for offline evaluation and 37% for online evaluation. The reviewed pages do not state the survey’s sample size or field dates, so these figures should be read as LangChain-reported survey results, not universal estimates or a direct measure of every organization’s practice. See LangChain’s AI Observability in the Agent Development Lifecycle and its March 3, 2026 explainer.
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