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To monitor an AI agent across tools, capture its whole execution as a linked trace: the incoming request, agent steps, model calls, retrieval, tool calls, handoffs, delegated work, errors, and outcomes. Pair those traces with operational metrics and quality and safety evaluations. Then decide what trace data to retain and who can see it. A model-call log alone will not show what an unrecorded tool did, and tracing explains actions but does not prevent unauthorized ones.
What an agent trace needs to show
A useful trace follows control through a run, rather than recording only the final answer or a sequence of unrelated model requests. Start with the user request or other run entry point, then connect the work that follows under a stable run or session identifier.
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- Agent work: the root agent and, when applicable, delegated agents, with parent-child relationships that show how work was assigned and returned.
- Model activity: generations and their durations, linked to the agent step that initiated them.
- Retrieval: retrieval activity and the relevant datastore or retrieval operation.
- Tool activity: tool identity, recorded arguments, result or error, and execution outcome.
- Control flow: handoffs, guardrails or other recorded events, failures, and the run outcome.
The OpenTelemetry GenAI semantic conventions provide shared attribute concepts for providers and models, messages, retrieval, tool definitions, tool arguments, and tool results. They distinguish tool types such as agent-side external API tools, client-side functions, and datastore tools. Agent-specific conventions and instrumentation remain an evolving area, so check which convention and integration versions your deployment actually uses.
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Build monitoring in four steps
1. Set the trace boundary and preserve relationships
Begin at the run entry point and propagate a stable run or session identifier and parent-child context through agent, model, retrieval, and tool operations. For delegated work, keep the relationship to the parent and root agent so an investigator can follow the chain. Record enough about a tool invocation to identify which tool ran, what arguments were recorded, and whether it returned a result or error.
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OpenAI’s Agents API documentation describes sessions containing turns and traces grouping spans for agents, model responses, tools, and delegated agents. It also documents OTLP JSON trace export when trace export is enabled for the organization. The Agents SDK documentation describes built-in records for generations, tool calls, handoffs, guardrails, and custom events. These capabilities apply to the documented OpenAI paths; they do not by themselves establish that other components in an application appear in the trace.
2. Check that every production boundary is instrumented
Inventory the framework, model clients, tools, retrieval systems, and delegation mechanisms actually used in production. Run a representative task and inspect its trace against that inventory. If a tool or retrieval operation is missing, determine whether a supported integration covers it or whether the application needs to emit a manual span. Built-in instrumentation in one framework does not guarantee coverage of every component in the execution path.
OpenTelemetry describes both built-in and external instrumentation approaches. AWS OpenSearch documentation describes hierarchical traces across orchestration, model calls, tools, and retrieval, with auto-instrumentation for named frameworks and providers. Check current integration lists and compatibility against the versions in your deployment; framework support can change.
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3. Use metrics to find patterns and traces to investigate runs
Traces help explain an individual execution. Metrics help identify changes across many executions. Track latency, errors, request and tool-call volume, token usage, and run status. Separately evaluate outcome quality, safety, groundedness, and whether tools were used correctly. Establish expected baselines and alert on meaningful deviations in both operational behavior and evaluated outcomes.
Microsoft’s guidance on observability for generative AI and agentic AI systems cautions that “Uptime and error rates are not good indicators of quality and reliability in AI systems.” A service can be available and return technically successful responses while still using the wrong tool or producing poor, unsafe, or unsupported answers. Use quality and safety evaluation alongside infrastructure and execution signals.
4. Set trace-data rules before retaining payloads
Message content, retrieval queries, system instructions, tool arguments, and tool results may contain sensitive information. Decide which attributes to collect, who may access them, and how long they are kept. Filter or truncate payloads where feasible, and apply access controls, encryption, data minimization, and the applicable data-residency and compliance requirements. Microsoft’s guidance recommends data contracts that balance forensic needs with privacy and legal obligations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose an instrumentation and observability approach
The following are documented capabilities, not an independent product test or a complete market comparison. Their scope differs: instrumentation conventions, framework-specific tracing, a hosted observability service, and policy hooks solve related but distinct problems.
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|---|---|---|
| OpenTelemetry instrumentation with a compatible backend | Shared GenAI telemetry conventions and an evolving approach to agent instrumentation; instrumentation may be built in or external. | Whether every framework, model, tool, and retrieval boundary is covered; whether data can be exported to the chosen backend; and how sensitive payloads are controlled. |
| OpenAI Agents tracing | The SDK documents records for generations, tool calls, handoffs, guardrails, and custom events. The Agents API documents session and turn views, span details, and OTLP JSON export when enabled for the organization. | Whether the organization’s retention policy permits tracing, export is enabled, and non-OpenAI application components are represented. |
| AWS OpenSearch AI observability | AWS documents hierarchical traces spanning orchestration, model calls, tools, and retrieval, with GenAI conventions and auto-instrumentation for named frameworks and providers. | Whether the current integration list covers the deployed stack and whether storage, access, and retention settings meet organizational requirements. |
| Policy hooks alongside tracing | Agent Control Standard version 0.1.0 describes pre-action hooks and traceable policy dispositions. | Whether preventive enforcement is needed and whether the deployment supports the standard and required conformance profile. Version 0.1.0 is an emerging standard, not evidence of universal implementation. |
Compare framework coverage, portability, export controls, privacy and retention, cost, and deployment fit before selecting an approach. Confirm current availability and limits with the relevant provider. Documentation describes supported capabilities; it does not show that a given deployment has enabled or correctly configured them.
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Keep observation separate from enforcement
A trace can help explain an action during or after execution, but recording a tool call is not the same as authorizing it. If an action must be checked before it happens, use an enforcement mechanism at the action boundary. The Agent Control Standard version 0.1.0 describes pre-action hooks that can allow, deny, modify, ask, or defer an action and record the policy decision. Treat this as an emerging standard and verify support in the particular deployment; tracing remains valuable for visibility, but it is not itself a preventive control.
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