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Monitor an AI agent by tracing its entire run—not just the final answer—and by enforcing permissions outside the model. Link each model call, retrieval, tool call, handoff, approval, and error to a run; attribute costs and outcomes to that run; and make downstream services reject actions the user or agent is not authorized to take. Traces and alerts make behavior visible, but they do not prevent a forbidden action.
What should an AI agent monitor capture?
Create a trace for each task or session, then connect the operations that make up that run. A final response can look plausible even if an intermediate retrieval was wrong, a tool failed, or a retry changed the result. Linked spans let an operator follow the sequence and identify where it went wrong.
Record the execution path
- Model generations: Record the model and operation, timing, outcome, and token usage where available.
- Tool calls: Capture the tool, target, call status, timing, and enough of the arguments and result to diagnose behavior.
- Retrievals and application steps: Record retrieval operations and relevant custom steps, not only LLM activity.
- Handoffs and agent changes: Show when work passes to another agent or service, and preserve the link to the parent run.
- Controls and errors: Include guardrail or policy decisions, approval events, failed or timed-out operations, and retries.
- Correlation data: Use stable run and span identifiers so events can be followed across services.
OpenAI Agents SDK tracing documents events for generations, tool calls, handoffs, guardrails, and custom events. Langfuse documents tracing across LLM and non-LLM calls and agent workflows. These are examples of trace coverage, not a requirement to adopt either product.
Protect data in telemetry
Capture enough inputs, outputs, and tool arguments to investigate a run, but do not turn logs into an unprotected copy of your application data. Redact secrets and minimize personal or confidential content; restrict who can view retained traces. OWASP’s security guidance warns against logging sensitive information in plain text. Decide which fields are necessary for diagnosis and audit before enabling broad payload capture.
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How do you make agent errors visible and actionable?
Instrument errors at the span where they occur, and retain the run-level outcome. That distinction helps separate a model failure from a downstream tool, retrieval, or application failure.
Track failure patterns
- Failed, timed-out, or malformed tool calls.
- Model and retrieval errors, including where a run stopped.
- Retries and whether they succeeded, failed, or exhausted a configured limit.
- Latency by operation and total run duration.
- Completion outcome, such as completed, failed, stopped by a limit, or awaiting approval.
Build alerts around error rates, repeated retries, stalled or unusually long runs, and unexpected tool usage. Set thresholds from your application’s normal behavior and risk tolerance; there is no universal retry count or latency threshold that fits every agent. A useful alert identifies the affected run and operation, rather than only reporting an aggregate spike.
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How do you find and control cost overruns?
Measure usage per model generation and aggregate it to the run. A model-token subtotal is not necessarily the full cost of completing an agent task.
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Attribute the full run cost
- Record input, cached input, and output tokens for each generation when the API exposes them.
- Include retries and subagent calls in the parent task’s accounting, while retaining their individual spans for diagnosis.
- Account separately for applicable tool charges, sandbox or other compute, third-party services, and cache-write costs.
- Aggregate by run, agent, user, model, or task when those views help with investigation or budget ownership.
OpenAI’s Agents API documentation identifies input, cached input, and output tokens as cost contributors and says reasoning tokens are billed as output tokens. It also recommends accounting for subagent calls and retries, and notes that tools, sandbox compute, third-party services, and cache writes can add costs. Check the applicable pricing and usage records for the services you use rather than treating token totals as an all-in figure.
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Set boundaries that stop runaway runs
Configure application-level limits for maximum spend, steps, retries, and tool calls. Decide what should happen when a limit is reached—such as stopping the run, returning a controlled error, or requesting human intervention—and log that outcome. Alert as a run approaches a budget boundary if operators have time to intervene. OWASP identifies unbounded loops as a denial-of-wallet risk and recommends limits on tokens, cost, retries, and tool chains.
How do you stop unauthorized agent actions?
Separate observation from enforcement. A trace can show that an agent tried an action; only an authorization check at the system that executes the action can reliably allow or reject it. Do not ask the model to decide whether it is permitted to grant itself access.
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Scope tools and permissions
- Give each agent only the tools needed for its task.
- Limit each tool’s permissions and resource scope; avoid broad credentials when a narrower permission will do.
- Have the downstream application or service check the requesting user’s authorization for each sensitive operation.
- Fail closed when authorization cannot be established. A missing or unavailable policy check should not silently become permission to proceed.
OWASP’s Gen AI Security Project recommends implementing authorization in downstream systems rather than relying on an LLM to decide whether an action is allowed. Treat the agent as an untrusted requester of an operation, even when its earlier reasoning appears sound.
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Make approval specific to the action
Require explicit human approval for high-impact or irreversible operations. Bind the approval to the exact action being requested: the target resource and the normalized parameters should be clear to the reviewer, and execution should verify that the approved action is the one being attempted. If the target or parameters change, request approval again. Where possible, have a separate policy or execution component validate both authorization and approval before carrying out the action.
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Make sensitive actions auditable
For a high-risk action, connect the requesting user and agent identity to the tool, target resource, normalized parameters, authorization result, approval identifier, applicable policy version, and execution outcome. Alert on privilege changes, repeated approval-bypass attempts, unusual tool-call rates, and sudden changes in high-risk actions. OWASP’s Cheat Sheet Series recommends logging agent decisions, tool calls, and outcomes, and requiring explicit approval for high-impact or irreversible actions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should teams review traces, policies, and audit logs?
Review trace data alongside authorization decisions and the downstream system’s audit log. A dashboard can show that an action occurred; by itself, it cannot establish that the action was authorized or prevent the next attempt.
- Follow a run end to end. Confirm that its model, retrieval, tool, handoff, error, and approval events can be connected using stable identifiers.
- Reconcile sensitive actions. Compare the agent trace with the downstream audit record and confirm that identity, target, parameters, decision, and outcome agree.
- Exercise rejection paths. Test that prohibited actions fail closed, including when the policy service is unavailable or the agent changes its request.
- Test approval binding. Confirm that an approval applies only to the reviewed action and cannot be reused for a different target or set of parameters.
- Test run limits. Verify that configured spend, retry, step, and tool-call limits actually stop or pause execution as intended.
- Review alerts and access. Check that alerts lead to actionable run details and that only appropriate staff can inspect sensitive telemetry.
OWASP recommends independent validation, approval controls, audit trails, and adversarial testing. Treat monitoring and authorization as complementary controls, not substitutes for one another.
Which observability option should you compare?
Choose based on the events you need to see and the systems already in your stack. The following table summarizes capabilities described in the products’ documentation and overviews reviewed on October 4, 2026. It is not a ranking or a claim of feature parity.
| Option | What its reviewed documentation describes | Useful comparison question |
|---|---|---|
| OpenAI Agents SDK tracing | Trace events for generations, tool calls, handoffs, guardrails, and custom events. | Does its event coverage and trace access fit agents built with this SDK? |
| Langfuse | Traces across LLM and non-LLM calls, agent sessions, usage and cost views, dashboards, alerts, and OpenTelemetry-related integrations. | Does its trace flexibility, deployment approach, integration, and cost or quality view fit your needs? |
| Datadog Agent Observability | Agent traces with cost, latency, token usage, and errors within Datadog’s broader monitoring environment. | How well does it fit your existing Datadog APM and monitoring workflows? |
| LangSmith | End-to-end LLM and agent traces, cost and latency metrics, dashboards, and OpenTelemetry integration in its product overview. | Does it fit your LangChain workflows and existing telemetry? |
Before choosing, verify current capabilities, retention, data handling, deployment options, and pricing directly with each provider. The documented feature descriptions do not establish comparative pricing, feature parity, or a universally best platform. Observability products improve visibility; they do not replace downstream authorization or approval controls.
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