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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteAI agent observability is the practice of capturing and analyzing what an AI agent does across an entire run—not just the text it returns. It connects model calls, tool use, retrieval, errors, timing, resource use, and quality signals so teams can understand unexpected behavior, diagnose failures, and improve reliability.
Why an agent run is more than a model request
A basic chatbot exchange may look like one request and one response. An agent can take a variable, multi-step path: it may call a model more than once, retrieve information, invoke tools, contact services, and then produce an answer. Looking only at the final response—or at whether the service is online—can hide where a mistake occurred.
Observability follows the end-to-end run and links its component steps. A trace represents that run; nested spans represent work within it, such as a model invocation, retrieval operation, tool call, or supporting service request. A session can group multiple related traces from a conversation. This structure helps distinguish, for example, an incorrect model decision from a tool error or unsuitable retrieved context. AWS explains traces and sessions for agents.
What observability helps teams find
Agent behavior can vary from run to run. An agent may hallucinate, drift, regress after a change, fail in a tool interaction, or take an unexpected action. A final answer may show that something went wrong without revealing whether the cause was the model response, retrieved material, an error, a tool, or the orchestration path.
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Trace-level evidence gives developers a way to locate and reproduce problems. Combined with evaluation and production monitoring, it can also show whether changes improve quality, safety, performance, or reliability rather than merely changing the agent’s behavior. Google Cloud describes observability for generative AI systems.
The main signals: traces, logs, metrics, and evaluations
No single signal answers every question. A useful observability setup brings together operational evidence about what happened and evaluation evidence about whether the result was good.
| Signal | What it shows | Questions it helps answer |
|---|---|---|
| Traces and spans | The linked execution path of a run, including model calls, retrieval, tools, and services. | Which step caused a delay, error, or surprising action? |
| Logs | Events and errors recorded as the system operates. | What error or notable event occurred? |
| Metrics | Aggregated measures such as latency, token use, failures, and resource consumption. | Is performance or failure frequency changing across runs? |
| Evaluations | Quality assessments such as correctness, factuality, helpfulness, or policy outcomes. | Did the output meet the application’s expectations? |
For example, if an agent gives an incorrect answer, a trace can show which context it retrieved and which tools it called. Logs may reveal a failed service request; metrics can show whether that step is also a common source of latency; an evaluation can assess the answer against the task’s criteria. Google Cloud’s overview covers these observability signals.
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What to capture in an agent trace
Instrument each run so its important work can be followed across components. The goal is not to record everything indiscriminately: capture enough context to understand behavior while applying appropriate privacy and access controls.
- Correlate model, tool, retrieval, and supporting-service steps in one trace.
- Use nested spans to show the sequence and timing of significant operations.
- Record relevant inputs, outputs, and attributes where they are useful for diagnosis and appropriate to retain.
- Log events and errors that explain failures or unusual conditions.
- Measure end-to-end and per-step latency, error rates, token use, and resource consumption.
- Add evaluation signals for correctness, factuality, helpfulness, quality, or safety outcomes that matter to the application.
- Review both individual runs and aggregate production behavior.
How evaluation turns traces into improvement
Telemetry shows what the agent did; evaluation helps determine whether it did the right thing. Teams can inspect traces from real runs, score outputs, build representative datasets, and compare system or prompt revisions. That makes quality work more systematic than relying on a few memorable examples.
A practical loop is to use traces to identify a failure pattern, add or select representative examples, assess candidate changes against those examples, and monitor subsequent production behavior. The evaluation criteria should reflect the application: a factual answer may need a different assessment from a tool action that must comply with a policy. AWS documents agent trace inspection and evaluation workflows; OpenTelemetry discusses agent observability and evaluation.
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OpenTelemetry and instrumentation choices
OpenTelemetry is a useful interoperability starting point for collecting traces, metrics, and logs. Its agent observability guidance describes two common approaches: instrumentation integrated into an agent framework, or external OpenTelemetry instrumentation libraries.
Framework-integrated instrumentation
Built-in support can make setup more convenient because the framework provides instrumentation alongside its agent features. The trade-off is that teams depend on the framework’s coverage, conventions, and compatibility.
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External libraries can decouple observability from the agent framework and offer more control over how data is collected. They also bring dependencies and maintenance work, and can create compatibility problems if libraries, frameworks, or conventions diverge. The right choice depends on the coverage and control the team needs, as well as its ability to maintain the instrumentation. OpenTelemetry outlines these approaches.
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Agent observability standards are still evolving
Do not assume there is one settled, universal set of agent-specific telemetry conventions. OpenTelemetry’s March 2025 article describes semantic conventions for agents as ongoing work and cautions that its status may have changed; check the current conventions before choosing attribute names or asserting implementation status. OWASP’s Agent Observability Standard page is also marked as under development. It proposes traceability, inspectability, audit trails, and reuse of existing standards, but should not be treated as a finalized universal standard. OpenTelemetry’s article and OWASP’s project page describe that evolving landscape.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Protect sensitive data in traces
Prompts, model responses, and function-call inputs or outputs can contain personal, confidential, or otherwise sensitive information. Treat trace content as production data, not harmless diagnostic metadata.
The OpenAI Agents SDK documentation says sensitive-data capture is enabled by default in its tracing configuration and provides a setting to disable it. Google Cloud recommends considering Cloud Storage for prompts and responses rather than putting them in log entries; it notes that bucket objects support individual conversation deletion and can hold more data than a log entry. Decide what to collect, where it will be stored, who can access it, how long it will be retained, and how redaction and deletion work before enabling production traces. OpenAI Agents SDK tracing documentation; Google Cloud guidance.
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How to choose an observability approach
Compare approaches against the agent you actually operate and the workflows your team needs. A tool that captures model calls but misses retrieval or tools may leave the key failure path invisible.
- Coverage: Can it follow the agent, model calls, tools, retrieval, and supporting services?
- Interoperability: Does it use OpenTelemetry and current GenAI conventions, and can data reach your existing backends?
- Evaluation: Can the team score outputs, preserve representative datasets, and compare revisions or experiments?
- Operational workflow: Does it support local debugging, production monitoring, sessions, topology, and aggregate views?
- Data controls: Can sensitive content be excluded or redacted, with access, retention, and deletion controls that fit the application?
- Maintenance: Is instrumentation built in or externally maintained, and how will the team handle version compatibility and convention changes?
A sound starting point is an end-to-end trace for each run, operational signals for latency and failures, evaluation criteria tied to the application, and explicit controls for sensitive content. Expand coverage based on the failure modes and quality questions the system needs to answer.
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