An AI agent can be fast, available, and error-free—and still choose the wrong tool, rely on irrelevant context, or fail to complete the task. Traditional monitoring shows whether a service is healthy; agent observability must also show the sequence of decisions and actions that produced its answer. That means capturing traces and connecting them to evaluations and human review.
Why do agents need more than conventional monitoring?
Conventional application monitoring is built around known code paths and familiar health signals. An agent’s work may combine a natural-language request, model decisions, retrieved information, tool calls, and state that influences later steps. A system can meet its latency and uptime targets while taking an unsuitable action or returning an incorrect answer.
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For that reason, the useful diagnostic unit is often the execution trajectory, not just a single request and response. OpenTelemetry authors describe agents as LLM-enabled applications that use tools and high-level reasoning toward a goal, and argue that telemetry can support both operational troubleshooting and evaluation. Their March 6, 2025 article also notes that agent telemetry conventions were still evolving and warns that the article may be outdated. Read the OpenTelemetry article.
What is the difference between LLM monitoring and observability?
Monitoring tracks known signals and alerts on conditions a team has defined. For an LLM-powered system, those signals can include latency, errors, token usage, and cost. They are important for detecting operational problems, but they do not necessarily explain why an agent produced a particular result.
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Observability adds the evidence needed to investigate behavior. A trace can preserve the sequence of model and tool calls, the context supplied to them, intermediate results, and the final output. When a response is wrong despite normal service metrics, that trajectory can help a team locate the failure: for example, a retrieval problem, a poor tool choice, or a later step that mishandled an earlier result.
What should an agent trace contain?
A useful trace should make it possible to follow an execution from its inputs to its outcome. Capture the details needed to understand behavior, while applying appropriate access controls and data-retention policies to sensitive prompts, retrieved material, and outputs.
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- Inputs and outputs: the request, relevant prompt or instruction context, and the result returned at each step.
- Model calls: the model used, its inputs and outputs, and relevant call metadata.
- Tool activity: tool names, arguments, results, and errors, so the agent’s actions can be inspected.
- Retrieved context and intermediate results: the information and state that shaped later decisions.
- Timing and failures: step durations, errors, and other operational details that help distinguish a behavioral failure from an infrastructure problem.
- Feedback: evaluation results or human review associated with the execution.
It helps to distinguish the levels of recorded activity. A run is one execution step, such as an LLM call with its inputs, outputs, and related metadata. A trace is an ordered set of runs for one execution. A thread groups traces across a multi-turn interaction. Thread context matters when a later failure depends on what the user said—or what the agent did—in an earlier turn. LangChain’s educational material describes these concepts and their use in agent evaluation. Read its guide to LLM observability and monitoring.
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Observability records what happened; evaluation judges the captured behavior. An evaluation may produce a score, label, or decision. It can be run offline against a fixed dataset, online against production traces, or ad hoc after a team notices a failure pattern.
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The right evaluation unit depends on what went wrong. A single-step check may be enough to assess a tool choice or routing decision. If success depends on retrieval, tool use, and state changes across several steps, evaluate the full trace. If the question is whether the agent achieved a conversation goal or retained context across turns, assess the thread. LangChain’s guidance notes that larger evaluation units can be harder to construct and score; that is practical guidance, not a universal performance benchmark.
Use the trace to find and describe a failure, then choose an evaluation that tests the relevant behavior. When the failure is understood, turn it into a dataset example and a repeatable regression check. This creates a feedback loop: production evidence informs tests, and those tests help reveal whether a change has improved or regressed behavior.
Can you evaluate an AI agent without ground truth?
Some judgments do not have a simple, objectively correct reference answer. A team can still assess behavior with a rubric, a score, or a human decision, but it should be clear about what that result means. For ambiguous judgments, new failure modes, and cases where an automated evaluator has not been calibrated, human review remains important.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When should a team introduce AI agent observability?
Introduce it when teams need to diagnose outcomes that ordinary request-level metrics cannot explain, especially once an agent’s work spans multiple model calls, tools, or turns. The goal is not to collect every possible field; it is to preserve enough context to investigate consequential failures and evaluate the behavior the product depends on.
- Define the outcomes that matter and the failure types the team needs to detect.
- Instrument the path through model calls, tools, retrieval, and state changes, not only the final response.
- Choose whether evaluations belong at the step, trace, or thread level based on the failure being tested.
- Keep human review available for uncertain or novel cases, and use reviewed findings to build regression examples.
How should teams compare observability approaches?
Compare systems on the workflow they support rather than treating a feature checklist as a product ranking. OpenTelemetry is a vendor-neutral, open-source framework for instrumenting, generating, collecting, and exporting traces, metrics, and logs. Its documentation says more than 90 observability vendors support it; that is the documentation’s support count, not a market-share statistic. See the OpenTelemetry documentation, last modified August 29, 2025.
- Trace depth: Can you inspect model and tool calls, relevant inputs and outputs, retrieved context, intermediate state, timing, errors, and feedback?
- Conversation context: Can individual steps be connected to a complete execution and, when needed, a multi-turn thread?
- Interoperability: Does instrumentation use OpenTelemetry, or is there a credible route into existing telemetry collectors and backends?
- Evaluation workflow: Can the team run offline, online, and ad hoc evaluations at the level suited to its failure cases?
- Human review and learning: Can reviewers inspect relevant context, apply rubrics, and turn understood production failures into dataset examples and regression checks?
LangChain announced end-to-end OpenTelemetry support in LangSmith on March 26, 2025. The announcement says teams can standardize tracing across their stack and route traces to LangSmith or other observability platforms, naming Datadog, Grafana, and Jaeger as examples. This is a vendor’s description of its integration, not independent evidence that it is the best or most complete option. Read the announcement.
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OpenTelemetry provides a portability-oriented telemetry foundation; individual products provide their own tracing and evaluation workflows. Agent semantic conventions have been described as evolving, so verify current convention status and product integrations before relying on a specific schema or capability.
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