Log an AI agent run as one correlated trace with nested spans for model calls, tool use, retrieval, handoffs, and important application steps. That structure lets you follow what happened in a single run, while metrics help reveal whether the same problem is happening across many runs. Just as important, decide up front which prompt and payload data can be recorded, redacted, retained, and accessed.
What to capture in an agent trace
Think of a trace as the record of one end-to-end task and spans as the operations within it. Each span should have a clear place in the parent-child tree so you can move from the user’s request to the operation that failed or produced an unexpected result.
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Trace and span fields
Use structured fields rather than a single free-form message. A practical baseline is:
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- Correlation: trace ID, span ID, and parent span ID. Add a request, session, or job identifier when it helps locate a run across systems.
- Identity: agent or service name and the operation name, such as
model.generate,tool.search, ormemory.retrieve. - Timing and outcome: start and end timestamps, duration, and a consistent status such as success, error, or timeout. Include safe error context when a span fails.
- Useful context: framework and model identity, configuration details, and usage metadata where relevant.
- Diagnostic data: inputs, outputs, or references to them only when needed and permitted by your data policy.
Keep names and status values consistent, namespace application-specific attributes, and propagate trace context through asynchronous work and service boundaries. OpenTelemetry’s GenAI semantic conventions are intended to improve interoperability across vendors, but the conventions and implementations can evolve; verify that your chosen instrumentation supports the fields you need. Its published examples include attributes such as gen_ai.system, gen_ai.request.model, and gen_ai.usage.input_tokens (OpenTelemetry, March 6, 2025; Amazon OpenSearch Service).
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Give each consequential operation its own span
- Model calls: record which operation and model ran, timing, outcome, and appropriate usage or configuration metadata.
- Tool calls: record the tool identity, safe diagnostic arguments or a reference to them, return status, duration, and error context. This is what helps distinguish a poor tool choice from a tool that was chosen correctly but failed.
- Retrieval and memory: expose searches, retrievals, and memory reads as spans. A weak or irrelevant result can explain a bad answer even when the model call itself succeeded.
- Handoffs and delegation: show which agent delegated work and which agent or subtask received it.
- Meaningful application steps: add custom spans or events for operations that affect the result, such as validation or an important state change.
OpenAI’s Agents SDK describes built-in tracing for generations, tool calls, handoffs, guardrails, and custom events, with nested spans and trace identifiers (Agents SDK tracing documentation). AWS likewise recommends tracing tool and memory operations and inter-agent handoffs (AWS guidance on observability for agentic AI).
How to find the failure in a trace
Use the trace to diagnose an individual run, then use metrics and comparisons to determine whether the issue is isolated or recurring. A successful final response does not prove the run was healthy: the agent may have taken an inefficient path, repeated a step, or returned an incorrect answer without raising an exception.
- Locate one run. Find the trace using a stable request, session, or job identifier and select a failed or otherwise suspicious run.
- Follow the span tree in time order. Look for the first unexpected status, unusually slow operation, repeated call, or handoff that does not match the intended flow. Starting at the final answer alone can hide the upstream cause.
- Inspect the relevant span. Check its operation and tool identity, timing, safe inputs and outputs, and available error context. OpenAI’s trace UI documents step details, duration, status, and failed-span error details (Agents SDK tracing documentation).
- Check neighboring spans and their parent chain. This helps separate an agent decision from a downstream service or tool failure. For example, a trace can show that an agent selected a search tool, the tool returned an error, and a later step handled that result.
- Compare with other runs and metrics. Use aggregate signals to spot patterns such as rising latency or repeated failures. For semantic defects that do not throw an exception, attach an outcome label or evaluation so an incorrect result can be found rather than silently counted as a success.
- Add a regression case. Re-run the failure scenario and confirm the trace exposes the same failure class while respecting your data policy.
AWS’s agent debugging guide discusses issues including infinite loops and tool invocation failures, and presents dashboards, traces, and metrics as complementary ways to investigate them (AWS AgentCore debugging guide, June 29, 2026). Telemetry can also inform evaluation and system improvement, as described by OpenTelemetry (OpenTelemetry, March 6, 2025).
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The right path depends on your framework, existing platform, privacy requirements, and whether you need to move telemetry between backends. The available guidance describes capabilities and prerequisites, not an independent platform comparison or current comparative prices.
| Path | When it fits | What to verify |
|---|---|---|
| Framework-native tracing | A quick fit when your agent framework already captures the operations you need. The OpenAI Agents SDK documents tracing for generations, tools, handoffs, guardrails, and custom events, with a trace UI and export-related capabilities (Agents SDK tracing documentation; OpenAI trace grading). | Confirm exactly what is recorded and exported, which data controls apply, and whether organizational policy permits the feature. OpenAI documents that SDK tracing is unavailable for organizations using its APIs under a Zero Data Retention policy (Agents SDK tracing documentation). |
| OpenTelemetry-first | A useful option when you want a portable telemetry model and compatible backend choices. OpenTelemetry describes GenAI semantic conventions as an effort to standardize telemetry in a varied vendor landscape (OpenTelemetry, March 6, 2025). | Check current convention maturity and support in both your instrumentation and backend. Amazon OpenSearch Service documents OpenTelemetry integration and agent-trace exploration, including auto-instrumentation integrations for OpenAI, Anthropic, Bedrock, LangChain, and others (Amazon OpenSearch Service). |
| Cloud-integrated observability | Potentially convenient when your team already operates within a cloud platform and wants traces alongside its other telemetry. | Account setup, permissions, instrumentation, and query costs matter. AWS AgentCore documentation says CloudWatch Transaction Search must be enabled to view certain AgentCore traces; non-runtime agents require OpenTelemetry setup (AWS AgentCore tracing documentation). |
Before committing, verify framework and provider coverage, whether tool and retrieval spans are captured, what payloads leave your application, how context propagates across service boundaries, data retention, export format, and access controls. A product’s general claim of “agent support” does not establish that its instrumentation captures the operations your debugging depends on.
Make logging safe by design
Prompts, tool arguments, retrieved documents, and model outputs may contain personal, confidential, or otherwise sensitive data. Logging every payload can make a trace easier to inspect but create avoidable privacy and security risk.
- Decide which fields are necessary to diagnose failures; prefer metadata or references over full content where possible.
- Redact or minimize sensitive inputs and outputs, and apply access controls and retention limits to trace data.
- Preserve the identifiers, timing, operation names, and statuses needed to correlate spans even when payloads are omitted.
- Check provider and organizational policy before enabling payload capture. OpenAI documents configurable sensitive-data inclusion in some cases and the Zero Data Retention limitation noted above (Agents SDK tracing documentation).
AWS recommends PII-safe audit trails for agent systems (AWS guidance on observability for agentic AI). Treat this as an instrumentation requirement, not a cleanup task to defer until after traces are in production.
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Run deliberate failure scenarios before relying on a trace design. At minimum, check whether a reviewer can identify a bad tool choice, a failed or timed-out call, a repeated loop, a latency bottleneck, and an incorrect result that completed without an exception. For each case, confirm the relevant span, causal parent chain, status, and safe diagnostic context are visible. If a failure remains opaque, add instrumentation at the missing operation or improve the outcome label—not a blanket capture of every payload.
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