Monitor an AI agent by tracing the full run—not just its final answer—and linking model calls, tool calls, retrievals, handoffs, errors, usage, and authorization decisions to a shared run identifier. Then enforce permissions, budgets, and approval requirements in the systems that execute actions: traces can reveal what happened, but they cannot prevent an agent from doing something it was allowed to call.
What to monitor in an agent run
An answer can look correct even when an intermediate retrieval failed, a tool timed out, or a retry changed the result. A useful trace makes those steps visible in sequence and preserves their causal relationship. Create a trace for each task or session, then connect spans for model generations, retrievals, tool calls, handoffs, and application-side work.
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Give each run and span stable correlation identifiers so operators can follow activity across services. Capture timestamps, duration, completion status, errors, and retry information. Record enough input, output, and tool-argument detail to reproduce or understand a failure, but redact secrets and restrict access to personal or confidential information. Plain-text logs can themselves become a data exposure risk.
Link the execution path
OpenAI Agents SDK tracing documents events for generations, tool calls, handoffs, guardrails, and custom events. Langfuse documents traces spanning LLM and non-LLM calls and agent workflows. These illustrate the key requirement: the trace should connect the agent’s decisions to the application operations that followed, rather than leaving each system’s logs isolated.
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Record outcomes, not just activity
For each step, distinguish success, failure, timeout, cancellation, and retry. A tool-call span that says only “called” is not enough to tell whether the downstream action completed. Include the resulting status or error and, where relevant, the final run outcome.
Make errors and runaway behavior actionable
Use traces to separate model-side failures from retrieval, tool, and application failures. Track error events, failed or timed-out calls, latency, retries, and runs that stall or take unusually long. Configure alerts for patterns that matter to your application, such as rising failure rates, repeated retries, stalled runs, or unexpected tool use. Set thresholds from your own baseline; there is no universal error-rate or duration threshold that fits every agent.
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Alerts are most useful when they include the run identifier and the failing span, so an operator can move directly from the notification to the relevant execution path. Route recurring failures to owners of the model integration, tool, or downstream service rather than treating every agent error as a model problem.
Attribute cost to the complete run
Collect token usage and model cost for each generation, then aggregate by run and, where useful, by agent, user, model, or task. Count retries and subagent calls against the originating run; otherwise, a run that fans out or repeatedly retries can appear cheaper than it was.
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An LLM token subtotal is not necessarily the complete cost of an agent task. OpenAI’s Agents API documentation identifies input, cached input, and output tokens as cost contributors; reasoning tokens are billed as output tokens. Depending on the implementation, also account for tool charges, sandbox compute, third-party services, and cache writes.
Set limits that stop cost growth
Define application-level boundaries for maximum spend, steps, retries, and tool calls. Alert or stop execution when a configured boundary is reached. OWASP identifies unbounded loops as a denial-of-wallet risk and recommends limits on tokens, cost, retries, and tool chains. Treat these controls as runtime safeguards, not merely dashboard metrics.
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Prevent unauthorized actions at the point of execution
Observability and enforcement are separate jobs. Give an agent only the tools and resource scope needed for its task. For each sensitive operation, the downstream application or service should check the requesting user’s authorization. Do not rely on the model to decide whether the user is entitled to an action.
Require explicit human approval for high-impact or irreversible actions. Bind the approval to the exact action being requested—its target and normalized parameters—not to a vague instruction such as “approve this task.” Where possible, have a separate policy or execution component validate the action and approval immediately before execution.
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Make sensitive actions auditable
For high-risk operations, log the agent and user identity, tool, target resource, normalized parameters, authorization result, approval identifier, execution outcome, and applicable policy version. OWASP’s Agentic AI security guidance calls for logging decisions, tool calls, and outcomes, and for approval of high-impact or irreversible actions. Redact sensitive values where possible while retaining enough information for an authorized reviewer to understand what was requested.
Alert on privilege changes, repeated attempts to bypass approval, unusual tool-call rates, and sudden changes in high-risk actions. Review those alerts alongside downstream audit logs and policy decisions: a monitoring dashboard can show that an action occurred, but it cannot establish by itself that the action was authorized or block the next one.
Test the controls, not only the dashboard
Exercise the agent with prohibited actions, missing permissions, failed tools, retries, and budget exhaustion. Verify that unauthorized requests fail closed, an approval applies only to the action it covered, and configured limits actually stop a runaway run. OWASP recommends independent validation, approval controls, audit trails, and adversarial testing.
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- Confirm each run has a trace that links model, retrieval, tool, handoff, and application steps.
- Check that failures, timeouts, retries, and final outcomes are visible and attributable.
- Compare run-level cost with underlying generation usage and relevant non-model charges.
- Attempt sensitive operations without authorization or approval and verify they do not execute.
- Confirm logs are access-controlled and sensitive data is not exposed unnecessarily.
Choose observability tools by coverage and fit
Observability platforms can improve trace visibility, cost attribution, alerting, and integration with existing telemetry. They do not replace downstream policy enforcement. The following are documented examples, not a ranked comparison; the available documentation does not establish feature parity, comparative pricing, or one universally best choice.
| Option | Documented capabilities | Useful selection question |
|---|---|---|
| OpenAI Agents SDK tracing | Trace events for generations, tool calls, handoffs, guardrails, and custom events. | Does its event coverage fit the SDK and trace-access needs of your implementation? |
| Langfuse | Traces across LLM and non-LLM calls and agent sessions, usage and cost views, dashboards, alerts, and OpenTelemetry-related integrations. | Do its trace flexibility, deployment options, integrations, and cost or quality views fit your environment? |
| Datadog Agent Observability | Agent traces with cost, latency, token usage, and errors in a broader monitoring environment. | Would using it alongside your existing Datadog APM and monitoring simplify operations? |
| LangSmith | End-to-end LLM and agent traces, cost and latency metrics, dashboards, and OpenTelemetry integration. | Does it fit your LangChain workflows and existing telemetry? |
Before selecting a platform, verify its current capabilities, retention, data handling, deployment options, and pricing directly. Evaluate whether it can trace the application steps and downstream decisions your team needs to inspect—not only model calls.
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