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To monitor an AI agent across tools, record its full execution as a linked trace: the request, agent steps, model generations, retrieval operations, tool calls, handoffs, errors, and outcome. Then pair those traces with service metrics and quality and safety evaluations. A trace helps explain what happened; it does not, by itself, prevent an agent from taking an unauthorized action.
What an agent trace needs to show
A final answer is not enough to diagnose an agent run. The useful unit is the end-to-end execution, represented as a root trace with related spans for the work performed along the way. Each span should identify an operation, its timing and status, and its relationship to the parent operation.
For a typical run, that means capturing the user request or other entry point, the root agent, model generations, retrieval activity, tool calls, delegated agents, handoffs, errors, and the final run outcome. For tool operations, record the tool identity and execution result so an investigator can distinguish a successful call from a failed one or a call that never occurred. Where delegated agents are involved, retain parent-child relationships back to the root run.
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How to build coverage across your execution path
1. Define the run boundary and propagate context
Start a trace at the user request or other point where a run begins. Propagate a stable run or session identifier and parent-child context through agent steps, model clients, retrieval systems, tools, and delegation. This is what lets an investigator move from an individual tool action back to the decision and request that led to it.
OpenTelemetry’s GenAI semantic conventions provide shared attribute names for model and provider details, input and output messages, retrieval data, tool definitions, tool-call arguments, and tool results. They describe tool categories that include agent-side external API tools, client-side functions, and datastore tools. Treat the conventions as a common vocabulary, and check the convention and instrumentation versions used by your deployment because agent-specific coverage is still evolving.
2. Inventory what runs in production
List the framework, model clients, retrieval systems, tools, and delegation mechanisms used by the application. Run representative workflows and inspect the emitted spans. If a component is absent, determine whether a supported integration covers it or whether you need to add manual instrumentation.
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3. Check export and backend behavior
Confirm that traces reach the system where operators will search and inspect them, and that the export path preserves the relationships and attributes needed for investigation. OpenAI’s Agents API documentation describes an OTLP JSON trace-export path when trace export is enabled for the organization. That availability is conditional; check the relevant organization settings and data-retention policy rather than assuming export is active.
Monitor reliability with metrics and evaluations
Use traces to investigate particular runs and aggregate metrics to detect patterns across many runs. Track operational signals such as latency, errors, request volume, tool-call volume, token usage, and run status. These show whether the system is slow, failing, or consuming resources differently from its baseline.
Operational health is not the same as agent quality. Evaluate outcomes separately for dimensions that matter to the task, such as answer quality, safety, groundedness, and whether the agent selected and used tools correctly. Microsoft cautions that uptime and error rates alone are poor indicators of quality and reliability in AI systems. Establish expected baselines and alert on meaningful changes in both operational signals and evaluated outcomes.
Choose an observability approach
The options below reflect capabilities described in the cited organizations’ documentation; they are not a complete market comparison or independent product test.
Best Value
| Approach | Documented capability | Questions to resolve |
|---|---|---|
| OpenTelemetry instrumentation with a compatible backend | GenAI telemetry conventions and an evolving approach to agent instrumentation; instrumentation may be built in or added externally. | Does coverage include every framework, model, tool, and retrieval boundary? Can data reach the intended backend? How are sensitive payloads handled? |
| OpenAI Agents tracing | The SDK documents tracing for generations, tool calls, handoffs, guardrails, and custom events. The Agents API documentation describes session and turn views, span details, and OTLP JSON export when enabled for the organization. | Does the organization’s data-retention policy permit tracing? Is export enabled? Are non-OpenAI components in the application represented? |
| AWS OpenSearch AI observability | AWS documents hierarchical traces across orchestration, model calls, tools, and retrieval, along with GenAI conventions and auto-instrumentation for named frameworks and providers. | Does the current integration list cover the deployed stack? Do 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. | Is preventive enforcement required? Does the deployment support the standard and the conformance profile you need? |
Compare framework coverage, portability, export controls, privacy and retention, cost, and deployment fit. Confirm current product availability and limits with the relevant provider before choosing.
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Trace payloads can contain more than operational metadata. OpenTelemetry’s GenAI registry warns that message content, retrieval queries, system instructions, tool arguments, and tool results may contain sensitive information. Decide what to collect before enabling detailed payload recording, not after traces have accumulated.
- Specify which attributes and payloads are necessary for debugging and evaluation, and filter or truncate content where feasible.
- Define who may access traces and how long they are retained.
- Apply data minimization, access controls, and encryption, and account for applicable data-residency and legal requirements.
- Document the balance between forensic needs and privacy obligations in a data contract.
Microsoft’s guidance emphasizes data contracts that address privacy, residency, minimization, retention, legal obligations, access control, and encryption. Apply those decisions to the telemetry backend and any export destinations, not only to the agent application.
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Tracing provides visibility into actions and their context. It can help explain a decision during investigation, but logging a tool call does not stop the call from happening. If an action must be authorized before execution, enforce that rule 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. It is an emerging standard, so verify that a chosen framework supports it and the required conformance profile. Regardless of standard, keep the control decision distinct from the trace that records what occurred.
Quick Recap
A practical review checklist
- Can an operator follow a run from its entry point through model, retrieval, tool, and delegated-agent activity?
- Do spans preserve parent-child context, operation timing, status, and tool outcomes?
- Have representative workflows been checked for missing instrumentation boundaries?
- Are operational metrics paired with quality, safety, groundedness, and tool-use evaluations?
- Are payload collection, access, retention, filtering, export, and residency rules defined?
- Are actions that require approval or denial guarded by enforcement rather than relying on trace review?
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