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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchTo diagnose an AI agent run, trace the whole workflow—not just the final model response. Record model calls, tool invocations, handoffs, retrieval, and guardrail decisions in a correlated trace, then use structured logs, metrics, evaluations, and security monitoring to explain failures and spot harmful changes. Treat prompts, responses, and tool data as sensitive: capture only what you need under explicit access, retention, and redaction rules.
What should an AI agent trace include?
Represent each user request or background job as a trace, with spans for the work that makes up the run. A model-only trace misses important causes of unexpected behavior: a tool may return bad data, a handoff may route to the wrong agent, or a guardrail may block an otherwise valid action.
Instrument the components that participate in the workflow:
- Model generations and other model calls.
- Retrieval and the provenance of the sources returned.
- Tool invocations, including their identity and permission context.
- Agent handoffs, policy or guardrail checks, and consequential custom operations.
Where governance permits, attach service and agent identity, framework and model version, timestamps, and a run or conversation identifier. Microsoft’s observability guidance for generative and agentic AI systems recommends capturing request identity, run context, retrieval provenance, and tool details—including names, arguments, permissions, and outputs—subject to data governance. The OpenAI Agents SDK tracing documentation describes traces that can include generations, tool calls, handoffs, guardrails, and custom operations.
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How do you correlate agent logs and traces across services?
Use a consistent telemetry foundation where possible. OpenTelemetry provides the basics for traces, metrics, and logs, but conventions for agent-specific data are still evolving. Its 2025 overview of AI agent observability discusses both built-in instrumentation and instrumentation libraries. Check the convention version and exporter behavior for the framework you actually deploy; do not assume different frameworks emit identical fields.
Put trace and span identifiers on log records when supported, and include resource context such as the emitting service or deployment. OpenTelemetry’s logging specification identifies trace context and resource context as ways to correlate log records with spans and their source. That correlation lets an operator move from an error log to the part of a run—and the service—that produced it.
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For remote tools or MCP servers, verify that trace context crosses the boundary and that the receiving service records compatible spans. The Microsoft Agent Framework observability documentation describes propagation of OpenTelemetry trace context to MCP servers when an active span context exists. Treat propagation as something to validate in your own framework and service path, not as an automatic property of every remote call.
What belongs in logs, and should they contain prompts or responses?
Operational metadata can often explain a run without placing every prompt, response, or tool payload in a general-purpose log store. Before capturing content, decide what debugging or incident-response need it serves, whether it can be redacted or sampled, who may access it, where it is stored, and when it will be deleted. Microsoft recommends governing collection and retention through data contracts that balance forensic needs with privacy, data minimization, residency, retention requirements, and legal obligations in its AI observability guidance.
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Framework defaults differ, so inspect the exact SDK version and configuration rather than assuming sensitive content is excluded:
| Framework or service | Documented behavior | Practical implication |
|---|---|---|
| Microsoft Agent Framework | ENABLE_SENSITIVE_DATA is false by default; the documentation warns that enabling sensitive content can expose secrets. |
Keep the default unless a controlled development or test use case requires content, and define safeguards before changing it. See Microsoft’s documentation. |
| OpenAI Agents SDK for Python | trace_include_sensitive_data is true by default. Disabling it omits Responses API request input and response output from those spans. |
Review this setting explicitly before production tracing. See the SDK tracing documentation. |
| Google Cloud | Google recommends storing prompts and responses in Cloud Storage rather than log entries; its Cloud Logging documentation states a maximum log-entry size of 256 KiB. | This is a Google Cloud implementation detail, not a universal logging limit or a requirement for other backends. See Google Cloud’s AI agent observability documentation. |
For any backend, set content-specific access controls, redaction, retention, and deletion rules before turning on payload capture. A trace can be useful with identifiers, timings, outcomes, and carefully selected metadata even when full content is kept out of routine logs.
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Which metrics and evaluations should you monitor?
Traces help explain individual runs; metrics show whether system behavior is changing across runs. Build dashboards around the operational measures that match the service’s objectives:
- Latency, including relevant model and tool-call timing.
- Error rate and request volume, alongside tool-call volume where useful.
- Token use or cost signals when the framework and provider expose them.
- Evaluation outcomes such as groundedness, safety or risk, and correctness of tool use.
Establish a baseline for normal behavior and alert on meaningful deviations rather than treating every unusual action as an incident. Pair runtime telemetry with repeatable evaluations, regression runs, and release gates; logs and traces alone do not establish that an answer is accurate or safe. Microsoft’s guidance describes these evaluation dimensions and recommends security monitoring for relevant abuse cases, including prompt injection and data exfiltration. Collect enough governed context to investigate such events without making unrestricted content capture the default.
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How should you choose an observability backend?
Compare actual framework, language, and runtime support—not just a provider’s general OpenTelemetry claim. Check whether traces cover model calls, tools, handoffs, and downstream services; whether context propagates across boundaries; and how the backend handles sensitive content, access, retention, deletion, residency, evaluation, alerting, and operating cost. OpenTelemetry export can help preserve flexibility, but portability depends on the instrumentation and exporter covering the components in your stack.
Provider documentation describes different integration paths; it does not establish an independent performance or price ranking:
| Provider documentation | Documented integration examples | What to verify for your deployment |
|---|---|---|
| Amazon CloudWatch | OpenTelemetry traces from multiple agent frameworks and compute environments. | Framework and runtime coverage, trace completeness, exporter setup, and data controls for your account and region. |
| Google Cloud | OpenTelemetry instrumentation for LangGraph and ADK, with trace analysis. | Coverage of the frameworks and services in your run, plus content storage, access, retention, and deletion behavior. |
| Microsoft Foundry | Native tracing integrations for Microsoft Agent Framework and Semantic Kernel, with instrumentation paths for other frameworks. | Integration fit, trace fields, exporter configuration, data controls, and the behavior of the portal in your environment. |
These are examples of documented capabilities, not an apples-to-apples benchmark or endorsement. Choose against your workload and governance needs, then validate with a real end-to-end run.
How do you validate monitoring before relying on it?
- Generate a representative run. Include a model call, retrieval if used, at least one tool call, and any expected handoff or guardrail path.
- Inspect the trace in the selected backend. Confirm that model, tool, retrieval, handoff, and policy spans appear where expected, and that failures are visible.
- Follow the identifiers. Start from a log record and verify that its trace and span identifiers lead to the right spans, with resource context identifying the emitting service.
- Check service boundaries. If the run calls remote tools or MCP servers, verify that context propagates and that the receiving service records compatible telemetry.
- Review captured data. Confirm that prompts, responses, tool arguments, and results match your redaction, access, and retention policy.
- Exercise an evaluation and alert. Verify that operational metrics and quality or safety outcomes are collected and that alerts reflect meaningful deviations from your baseline.
Microsoft Foundry says traces typically appear in its portal within 2–5 minutes; that timing is specific to that service and can change. Use the Foundry tracing guide for its setup details, but confirm visibility and delay in the environment you operate.
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