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Monitor an AI agent by tracing each end-to-end run, recording what happens at every tool boundary, and combining those traces with event logs and aggregate metrics. A useful record connects the responsible agent, tool call, inputs, outcome, timing, errors, and session or workflow context—while limiting access to sensitive payloads.
Build one trace for each agent run
A trace is the end-to-end record of a unit of work. It contains related, timed spans for steps such as model generations, tool calls, retrieval, handoffs, guardrails, and service requests. Parent-child relationships show which operation triggered another, making it possible to reconstruct an agent’s execution path. AWS describes this trace-and-span model for agent monitoring in its CloudWatch agent monitoring guide.
Assign related turns and events stable identifiers, such as a trace ID plus a session, task, or workflow ID. Propagate the relevant correlation context across your agent framework, APIs, retrieval system, MCP servers, and other connected services. OpenAI’s SDK documentation describes trace IDs, optional group IDs, metadata, span start and end times, and parent IDs; AWS also models sessions alongside traces and spans. See OpenAI Agents SDK tracing and AWS CloudWatch agent monitoring.
Record the important details at every tool boundary
A tool boundary is where the agent calls an API, retrieval service, MCP server, or another connected system and receives an outcome. Capture enough information to answer what the agent tried to do, what happened, and what it did next.
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- Identity and context: trace ID, session or workflow ID, acting agent or subagent, and the parent operation.
- Action: tool name or endpoint, operation, and authorization context where relevant.
- Input and outcome: arguments or a safe representation of them, plus the returned result or outcome when available.
- Execution details: status, start and end times, duration, and error context. Include stack traces when they are useful and appropriate.
- State changes: relevant changes to internal agent state that help explain the action or a later decision.
OpenAI’s Agents API trace view documentation describes inspecting the tool called, arguments sent, and result when available. The Cyber Security Agency of Singapore’s agentic AI security addendum calls for logging actions, inputs and outputs, state changes, errors, timestamps, durations, and task, session, or workflow identifiers.
Do not equate a successful API response with correct agent behavior. Review the selected tool, its inputs and authorization context, the returned result, and the next action. Google Cloud’s agent developer guide recommends monitoring tool calls and exchanged data; its agent observability guide describes telemetry for examining communication paths to authorized agents, MCP servers, and external endpoints.
Use traces, logs, and metrics for different questions
These telemetry types complement rather than replace one another. Google Cloud describes them as separate inputs to agent observability, alongside prompt and response data used for quality assessment.
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|---|---|---|
| Traces | Reconstructing an individual run and its execution path | Parent-child steps, tool calls, retrieval, handoffs, timing, and failures |
| Logs | Finding discrete events and errors | Tool invocation, authorization failure, retry, or agent state change |
| Metrics | Watching aggregate production behavior | Latency, error rates, token use, and tool success or failure trends |
| Prompt and response data | Assessing output quality and behavior | Inputs and generated responses reviewed under appropriate access and retention controls |
Operational health is not the same as answer quality. A run can finish without errors and still produce a poor or unsafe result, so add evaluations or review labels for quality and safety alongside operational telemetry. Google Cloud’s observability guide distinguishes logs, metrics, traces, and prompt/response evaluation data.
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Set up a practical monitoring workflow
- Instrument the framework or tool boundary. Create a trace for each run and spans for model calls, tool invocations, retrieval, handoffs, guardrails, and relevant service calls. Confirm connected systems propagate trace or correlation context.
- Inspect individual traces. Reconstruct the operation sequence, parent-child relationships, timing, inputs, outcomes, and failures to investigate a specific run.
- Aggregate production metrics. Track latency, errors, token use, and tool success or failure so that trends are visible without opening each trace.
- Evaluate quality and safety separately. Add evaluations or review labels; do not rely on successful execution as a quality measure.
- Alert and review. Watch for errors, unusual tool use, long-running or looping workflows, and deviations from tested baselines. Periodically check whether tool permissions remain appropriate.
AWS frames monitoring around instrumentation, trace analysis, evaluation, and production health. Google Cloud identifies debugging failures and loops, latency, cost, quality, and security as uses of telemetry. The Singapore addendum recommends monitoring drift, tool permissions, and suspicious activity. See AWS CloudWatch, Google Cloud’s agent developer guide, and the Singapore addendum.
Choose an approach that fits your stack
There is no single best observability platform for every agent stack. Compare framework and provider support, visibility into tool calls and handoffs, export and interoperability, deployment fit, access controls and deletion options for sensitive data, and whether the tooling supports both individual-run debugging and aggregate health monitoring.
OpenAI Agents SDK
The SDK documentation says tracing is enabled by default and captures model generations, tool calls, handoffs, guardrails, and custom events. It also documents ways to disable tracing and states that tracing is unavailable for organizations using OpenAI APIs under a Zero Data Retention policy. Check the SDK tracing documentation for the behavior relevant to your configuration.
OpenAI Agents API
The API trace view organizes activity by session, turn, and span; a tool span can show the tool called, its arguments, and its result when available. Trace export uses paginated OTLP JSON and requires organization trace export to be enabled. Exporting existing traces does not configure automatic delivery of future traces. Details are in the Agents API tracing guide.
Best Value
AWS CloudWatch and Amazon OpenSearch Service
AWS documents OpenTelemetry instrumentation for several agent frameworks and deployment environments, with traces, spans, and sessions used for analysis. Its setup guidance is in CloudWatch agent telemetry setup and its agent monitoring guide. Amazon OpenSearch Service documents hierarchical traces across orchestration, model calls, tools, and retrieval, using OpenTelemetry GenAI attributes and instrumentation for multiple frameworks and providers; see OpenSearch AI observability.
Google Cloud
Google’s agent developer guide recommends OpenTelemetry. Its observability guide treats logs, metrics, traces, and prompt/response evaluation data as distinct inputs. For payload storage and a Cloud Logging limitation specific to its documented setup, see the data-handling section below and the developer guide.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Protect prompts and tool payloads
Prompts, model outputs, tool arguments, and results may contain sensitive information. Decide what to capture, who can inspect it, how long it is retained, and whether full payloads should be stored separately from operational trace metadata.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesIn its documented Google Cloud setup, the developer guide recommends storing prompts and responses in Cloud Storage rather than log entries. It notes that individual log entries cannot be deleted and that Cloud Logging has a 256 KiB maximum log-entry size. Those constraints apply to the described Cloud Logging setup, not to every telemetry platform. See Google Cloud’s agent developer guide.
The OpenAI SDK’s Zero Data Retention limitation applies to that SDK tracing feature. Verify current behavior and controls for your chosen SDK, storage service, and organization configuration in the relevant documentation, including OpenAI Agents SDK tracing. The sources cited here do not establish a legal retention period for every reader; requirements depend on jurisdiction, sector, data, and organizational policy.
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