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BasicAgent

AI Observability Platform Guide: Logs, Metrics, Traces, Evidence

AI observability blueprint—capture logs, metrics, traces, and Layered-CoT evidence per run to prove reliability and diagnose drift in agent workflows.

Observability for AI agents means every run has trace IDs, metrics, and evidence you can replay. This page uses the Multi-Agent-COT-Prompting patterns (Layered-CoT, sandbox-promote) to keep signals aligned with governance.

What you must capture

  • Logs: prompts, tool calls, responses, errors.
  • Metrics: latency, retries, fallbacks, eval pass rates, cost.
  • Traces: per-step spans with agent role, CoT checkpoints, and inputs/outputs.

Wiring the stack (code sample)

# Structured log for one agent step
log = {
    "run_id": run_id,
    "trace_id": trace_id,
    "agent": "governance_auditor",
    "step": "layered_cot_validation",
    "latency_ms": span.latency_ms,
    "retries": span.retries,
    "input_tokens": span.input_tokens,
    "output_tokens": span.output_tokens,
    "verdict": span.verdict,  # pass/fail
    "context": span.context_summary,
}
emit(log)  # send to your collector

How it connects to reliability

  • Sandbox → promote: tag spans as sandbox or promoted; only promoted outputs move forward.
  • Layered-CoT: treat each layer as a span; attach verdict + evidence to the trace.
  • RAV/RAC: store validation queries and corrections next to the step that produced them.

Signals to alert on

  • Spike in retries or timeouts (LLM/HTTP).
  • Drop in eval pass rate or fact-check score.
  • Increased cost per successful completion.
  • Drift in persona or prompt version (mismatched templates).

Where to send the data

  • Metrics: Prometheus/Grafana (latency, retry_rate, eval_pass_rate).
  • Logs/Traces: OpenTelemetry exporters (OTLP) with run_id + trace_id linking agents.
  • Evidence: append-only store for audit bundles (/llm-audit-trail-agent-pipelines/).

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