Distributed Tracing for AI Agent Actions

Distributed tracing connects a chain of events across multiple services into a single observable workflow. For AI agent systems, this means following a user request from the moment it enters the system, through orchestration, policy evaluation, tool execution, and response generation, across every agent and service involved.

Trace Structure

A trace consists of spans. Each span represents a unit of work: a policy evaluation, a tool call, an agent inference, or an API request. Spans have parent-child relationships that encode causality. The root span represents the original request. Child spans represent the work triggered by that request.

Trace: user-request-abc
  [orchestrator: 0-500ms]
    [policy-eval: 10-15ms]
    [research-agent: 20-300ms]
      [web-search: 50-250ms]
      [aegis-scan: 260-270ms]
    [writer-agent: 310-480ms]
      [policy-eval: 315-320ms]
      [generate-response: 325-470ms]

Propagating Context

The trace ID and parent span ID must propagate through every inter-service call. For HTTP-based communication, use the W3C Trace Context headers (traceparent, tracestate). For MCP tool calls, include trace context in the envelope metadata.

Authensor's action envelope includes a trace_id field. The control plane propagates this field through policy evaluation, Aegis scanning, and receipt generation, linking all safety operations to the originating trace.

What to Instrument

At minimum, instrument these operations:

• Action envelope creation
• Policy evaluation (include the policy version and decision)
• Content safety scanning (include scan duration and result)
• Tool execution (include tool name, duration, and outcome)
• Approval workflow events (request, response, timeout)
• Receipt generation

Analyzing Traces

Use traces to answer operational questions: Which step took the longest? Where did the safety check reject the action? How much latency does policy evaluation add? Which agent caused the error?

Sampling

High-traffic systems cannot afford to trace every request. Use head-based sampling (decide at the trace root whether to sample) or tail-based sampling (decide after the trace completes based on whether it contains errors or anomalies). Always trace 100% of requests that trigger safety alerts.

Distributed tracing transforms a multi-agent system from a black box into an observable workflow.