You can debug an AI agent with the same fundamentals you use for an API—logs, traces, metrics, and tests—but a single request-and-response view is rarely enough. An agent run may contain several model decisions, tool calls, handoffs, guardrail checks, retrieval steps, and state changes. Debugging it means reconstructing that whole run, finding the earliest point it went off course, then evaluating whether a change actually fixes the problem.
Why an agent run is different from one API request
A conventional API investigation often begins at a bounded boundary: inspect the request, response, status, and duration. An agent can make multiple model calls and invoke external tools before producing its final answer. It may also hand work to another agent, retrieve information, encounter a policy check, or update state along the way.
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OpenAI’s Agents SDK describes traces that can include LLM generations, tool calls, handoffs, guardrails, and custom events. Its evaluation guide describes a trace as the end-to-end record of those operations for one run. The practical consequence is that the final text is only one outcome of a larger execution path; it may not reveal which earlier decision or operation caused the failure. OpenAI Agents SDK tracing and OpenAI’s agent evaluation guide.
Google Cloud characterizes agent reasoning as nondeterministic and says telemetry is the reliable way to inspect decisions and tool selection. That is Google’s description of the problem, not a claim that every agent behaves unpredictably on every run. A run-level trace is useful because it records what actually happened rather than asking you to infer the path from the answer alone. Google Cloud: Observability for AI agent developers.
What to inspect in a useful trace
A trace should let you follow the root run through its important child operations, with enough context to distinguish an agent decision from an external service result. The right level of detail depends on your privacy policy and debugging need, but these are the main fields to consider:
- Run and span relationships: a trace or run identifier and parent-child relationships that preserve execution order and connect each operation to the overall run.
- Model calls: model identity and operation where permitted, plus prompt and response content only if necessary and allowed by policy.
- Tool calls: tool name and call identifier, relevant arguments, returned result or error, status, and duration.
- Workflow events: agent handoffs or delegation, retrieval steps and sources when relevant, guardrail or policy outcomes, and meaningful state transitions.
- Operational and quality signals: per-step and end-to-end latency, token or resource usage, errors, and evaluation results linked to the trace and the prompt, routing, tool, and guardrail versions.
Google Cloud recommends OpenTelemetry instrumentation and says Cloud Trace can extract events from spans that follow GenAI semantic conventions. Amazon OpenSearch also documents hierarchical agent traces and GenAI/OpenTelemetry conventions. Those are examples of approaches, not a guarantee that every framework or vendor captures every operation automatically. Check coverage for your chosen SDKs and add custom spans for important workflow events they do not expose. Google Cloud’s instrumentation guide and Amazon OpenSearch AI observability.
How to debug a failing run
- Select a representative run and define success. Record the failing case and state the expected outcome in observable terms: for example, which tool should be called, what result should be returned, or which policy must be respected.
- Follow the complete trace. Start at the root run and inspect model calls, tool invocations, retrieval, guardrails, handoffs, and other relevant workflow events. Confirm that operations are correlated and that instrumentation covers the steps you need to diagnose.
- Find the earliest unexpected event. Look for the first wrong tool choice, missing or incorrect context, failed external request, unintended handoff, policy failure, repeated loop, or latency bottleneck. Starting with the final answer can hide the point where the run first diverged.
- Separate agent behavior from service behavior. Check both the model’s decision and the context it received, then inspect the tool’s actual response and any side effect. A bad outcome can originate in the agent’s choice, the tool or service, or the interaction between them.
- Turn the failure into a check. Add a grader or explicit assertion that captures this failure class. Then compare changes to prompts, routing, tools, or guardrails against a stable set of representative cases. One successful replay does not establish that overall quality improved.
- Keep the evidence safe. Apply redaction or disable sensitive-content capture when required, keep secrets out of prompts and tool arguments, and review where exported telemetry is stored and how it is retained.
Use traces to locate problems and evaluations to judge fixes
A trace is evidence about execution, not a verdict on whether the answer was correct or useful. It can show that the agent called a particular tool, received an error, or handed off control. It does not by itself establish whether the run met the task’s requirements.
Use the trace to locate and understand the failure; use explicit expected outcomes, graders, or assertions to assess quality. When comparing changes, run evaluations over a stable dataset of representative cases and link their outcomes to the relevant traces and workflow versions. OpenAI’s evaluation guidance describes moving from individual trace inspection to datasets and repeatable evaluation runs. OpenAI: Evaluate agent workflows.
Correlate traces with logs, metrics, and quality signals
Different telemetry answers different questions. Google Cloud describes logs as event and error information, metrics as measures such as latency and token usage, traces as execution paths, and prompt or response data as input to quality assessment. Correlating them helps distinguish a slow external request from a repeated agent loop, or a service error from a low-quality decision. Google Cloud: Agent observability.
- Logs: What events or errors were recorded?
- Metrics: Where did latency or resource use rise?
- Traces: Which sequence of operations produced the outcome?
- Evaluation results: Did the run satisfy the task’s quality criteria?
Protect prompts, tool arguments, and trace storage
Tracing can capture content that should not be broadly retained. OpenAI’s Python SDK documentation says generation spans store model inputs and outputs and function spans store function inputs and outputs; its documented trace_include_sensitive_data option is enabled by default and can disable that capture. OpenAI also states that tracing is unavailable for organizations using its APIs under a Zero Data Retention policy. These are product-specific behaviors: confirm the SDK version and organization policy that apply to your deployment. 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 service-specific logging limit, not a general trace-size limit. Google Cloud: Observability for AI agent developers.
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Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Microsoft’s tracing guide recommends enabling content recording during development and debugging, then disabling it in production to protect sensitive data. It also warns against putting secrets, credentials, or tokens in prompts or tool arguments. The page reviewed describes tracing as generally available for prompt and hosted agents, while workflow and external agents are in preview; availability can change, so check the current product documentation for your framework and region. Microsoft Learn: Configure tracing for AI agent frameworks.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choosing an observability approach
Vendor-native tracing and an OpenTelemetry-centered setup are not mutually exclusive in every architecture. Compare them against the needs of your workflow and data policy rather than assuming a single tool will cover the whole run.
| Decision area | What to verify |
|---|---|
| Coverage | Can it capture model calls, tools, retrieval, handoffs, guardrails, state changes, and external services that matter to your run? |
| Correlation and workflow view | Can you reconstruct the end-to-end execution and connect child spans to the root operation? |
| Evaluation | Can you attach graders or outcomes to traces and run repeatable comparisons across a dataset? |
| Privacy controls | What content is captured by default? Can you redact it, control access, export it, and set retention or deletion policies? |
| Portability and effort | Which frameworks and providers are supported? Are semantic conventions and custom spans available, and how much instrumentation must you maintain? |
| Operational cost and limits | What are the effects of sampling, retention, telemetry volume, service-specific size limits, and instrumentation overhead? |
Use these questions to assess the versions, framework coverage, and data controls in your own environment. Documentation describes capabilities; it does not establish that one observability product is best for every agent or deployment.
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