Resilience & Recovery
Root Causes of AI Agent Failures That Reach Production
Production failures stem from broken tools and drifting context, not from weaker models.
Damien Fournier-Okafor · October 22, 2026 · 9 min read
Section
7 stories in Resilience & Recovery
Production failures stem from broken tools and drifting context, not from weaker models.
Preserve conversation transcripts and tool history to survive agent restarts intact.
A taxonomy of four recurring patterns can prevent most autonomous agent production failures.
Architectural patterns for surviving inevitable interruptions in multi-hour agent workflows.
Timeout cascades and duplicate writes are distinct failures requiring separate fixes.
LLM failures hide in silent quality degradation, not outages.
Autonomous agents hide failures in decision chains that standard observability tools can't see.