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FIELD REPORT · AI

Agent Observability Metrics That Matter

Tracing, autonomy, and outcome attribution signals for production AI agents.

PUBLISHED
September 14, 2025
UPDATED
May 15, 2026
READ TIME
1 MIN
AUTHOR
ONE FREQUENCY
KEY FACTS
Topic
ai, agents, observability
Published
September 14, 2025
Last updated
May 15, 2026
Read time
1 min
Word count
54

Robust agent observability spans reasoning trace capture, tool invocation spans, error taxonomy, and escalation pathways. Core metrics: task success rate, human intervention ratio, mean tool depth, hallucination exception frequency, cost per successful task, and latency percentile by reasoning depth.

We discourage vanity metrics (raw token count) and focus on decision quality + economic efficiency.

SOURCES

Cited and consulted.

  1. 01NIST AI Risk Management Framework (AI RMF 1.0)nist.gov
  2. 02Anthropic Research — Claude model capabilities and safetyanthropic.com
  3. 03OpenAI Platform Documentationplatform.openai.com
  4. 04Anthropic — Building Agents with Claudedocs.anthropic.com
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