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