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You can debug an AI agent with familiar tools, but a single request-and-response view is rarely enough. An agent run may include repeated model calls, tool executions, handoffs, retrieval, guardrails, and state changes. To find where it went wrong, inspect a correlated trace of the whole run, then evaluate the result against an explicit expectation.
Why an agent run is different from one API request
An API call often gives you a bounded unit to inspect: the request, response, status, and timing. An agent may make several model calls, choose among tools, receive tool results, transfer control to another agent, and continue with updated context. The final answer is only the end of that sequence; it may not reveal which earlier decision or operation caused a failure.
OpenAI describes agent traces as including LLM generations, tool calls, handoffs, guardrails, and custom events. Its evaluation guide defines a trace as the end-to-end record of model calls, tool calls, guardrails, and handoffs for one run. That makes the run, rather than an isolated call, a useful unit for diagnosis. This complements ordinary API debugging; it does not make request-level inspection obsolete. OpenAI Agents SDK tracing · OpenAI agent workflow evaluation
Google Cloud characterizes agent reasoning as nondeterministic and says telemetry is the reliable way to inspect an agent’s decisions and tool selections. That is Google’s description of the problem, not a guarantee that tracing alone explains every failure. Google Cloud: Observability for AI agent developers
What a useful trace should show
A trace should connect the run’s operations through parent-child relationships, so you can move from the overall execution to the particular model call, tool, or handoff that matters. Instrument what your workflow actually does, not just the model endpoint.
- Run identity and structure: a trace or run identifier, parent-child spans, and meaningful custom events.
- Model operations: model identity where permitted, operation status, duration, and prompt or response content only when necessary and allowed by policy.
- Tool operations: tool name and call identifier, relevant arguments, returned result or error, status, duration, and any meaningful side effect.
- Workflow transitions: agent identity, handoffs or delegation, retrieval steps and sources where relevant, and guardrail or policy outcomes.
- Run-level signals: end-to-end and per-step latency, token or resource usage, and evaluation outcomes connected to the relevant trace and versions of prompts, routing, tools, and guardrails.
Google Cloud recommends OpenTelemetry instrumentation and says Cloud Trace extracts events from spans that conform to GenAI semantic conventions. AWS documents hierarchical agent traces and GenAI/OpenTelemetry conventions for Amazon OpenSearch Service. These are examples of approaches to investigate, not a claim that one vendor or framework covers every component of every agent. Amazon OpenSearch Service AI observability
How to debug a failing run
- Choose a representative failure and define success. Record the case you want to diagnose and state the expected outcome in terms you can check—for example, the correct tool result, a required handoff, or a policy-compliant response.
- Open the whole run trace. Start at the root operation and follow model calls, tool invocations, retrieval, guardrails, and handoffs. Check that the relevant operations are instrumented and correlated; a missing span can make a broken sequence look complete.
- Find the earliest unexpected event. Look for a wrong tool choice, missing or incorrect context, a tool error, an unintended handoff, a guardrail failure, a loop, or a latency/resource bottleneck. Starting with the final text alone can hide the earlier cause.
- Separate the agent’s decision from the operation’s result. Inspect what the model received and chose, then inspect the tool’s actual response and side effect. A correct tool choice can still be followed by a failed external operation; a successful tool call can still be used incorrectly by the agent.
- Turn the failure into a repeatable check. Add a grader or explicit assertion for the failure class. Compare prompt, routing, tool, or guardrail changes against a stable set of representative cases. A successful replay by itself does not establish that quality improved.
- Protect the trace data. Redact or disable sensitive-content capture where appropriate, keep secrets out of prompts and tool arguments, and review export destinations, access, and retention.
Google Cloud recommends correlating telemetry types rather than relying on a single signal: logs show events and errors, metrics help surface latency and token usage, traces show execution paths, and prompt/response data can support quality assessment. Google Cloud: Agent observability
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Inspecting a trace helps locate the behavior that produced an outcome. It does not, by itself, tell you whether the outcome met the task’s requirements. Define expected results or use graders to assess quality, then connect those judgments to traces. For changes you want to compare, move from one-off inspection to datasets and repeatable evaluation runs. OpenAI’s workflow evaluation guidance describes trace grading and this progression toward broader evaluations. OpenAI agent workflow evaluation
Choose observability by coverage, control, and portability
Vendor-native tracing and an OpenTelemetry-centered setup are not mutually exclusive in every environment. Compare approaches against your workflow and data policy rather than assuming that a trace viewer captures everything you need.
| What to compare | Questions to ask |
|---|---|
| Coverage | Does instrumentation include model calls, tools, retrieval, handoffs, guardrails, state transitions, and external services used by this workflow? |
| Correlation and workflow view | Can you reconstruct a run and connect each child operation to its parent? |
| Evaluation | Can you attach outcomes or graders to traces and run repeatable comparisons? |
| Privacy controls | What content is captured by default? Can it be redacted or disabled? How are retention, deletion, export, and access controlled? |
| Portability and effort | Which frameworks and providers are supported? Are semantic conventions, custom spans, and exporter choices available for your needs? |
| Operational limits | What are the costs of telemetry volume and retention, and are sampling, latency, or service-specific size limits relevant? |
Handle trace content as sensitive data
Tracing can record more than timing and status. OpenAI’s Python SDK documentation says generation spans store model inputs and outputs, and function spans store function inputs and outputs; those fields can contain sensitive data. Its documented trace_include_sensitive_data option can disable that capture, which is enabled by default under the documented behavior. OpenAI also says tracing is unavailable to organizations using its APIs under a Zero Data Retention policy. Confirm the behavior for the SDK version and organization policy you use. OpenAI Agents SDK tracing
Google Cloud recommends storing prompts and responses in Cloud Storage rather than log entries when finer-grained control and deletion are useful. Its guide reports a 256 KiB maximum log-entry size for Google Cloud Logging; that is a logging limit, not a general limit for traces. Google Cloud: Observability for AI agent developers
Microsoft’s tracing guide says content recording can be enabled during development and debugging and recommends disabling it in production to protect sensitive data. It also advises against putting secrets, credentials, or tokens in prompts or tool arguments. The page describes tracing as generally available for prompt and hosted agents, while workflow and external agents are in preview; availability can change. Microsoft Learn: Configure tracing for AI agent frameworks
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For an API, request and response inspection may explain the behavior at the boundary you are debugging. For an agent, diagnose the sequence: trace the run, find the first divergence, inspect both the agent’s decision and the operation it triggered, and evaluate the outcome against explicit criteria. Keep enough telemetry to make that sequence understandable without capturing sensitive content unnecessarily.
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