Session Traces

Hold

Techniques

Execution logs that capture an AI agent's step-by-step runtime context for debugging.

Why it's here

Placed in Hold: 1 article(s) of evidence from 1 source(s), led by research-stage coverage, with 0 in the last 30 days. Confidence 24%. Low accumulated evidence, so it defaults conservatively pending more signal.

Evidence (1)

4InfoQ·9/11/2026research
Session Traces and Cost Controls Aid AI Agent Failure Debugging
The article highlights session traces and cost controls as important observability methods for diagnosing AI agent failures. These techniques help teams identify tool-call loops and runaway spending while retaining enough execution context for post-incident analysis.