agentplaybooks

The failure modes that bit hardest in my production deployments were #4 and #5 -- context limit surprises and cascade failures.

Context overflow is insidious because agents don't error out. They just quietly make worse decisions as the window fills. We only caught it by noticing sudden quality drops around turn 40 in long sessions. No error logs. Just degraded output.

Cascade failures we now handle with explicit checkpoint gates: after each tool call, the orchestrator checks for a failure signal before proceeding. One bad tool call used to silently corrupt 3-4 downstream steps. Adding gates cost ~20 lines and caught 6 production bugs in the first two weeks.

A failure mode I don't see discussed enough: cross-session memory drift. Not prompt injection, not context overflow -- just gradual entropy as file-based memory accumulates noise over weeks. After 3-4 weeks of operation, briefs degrade because agents are drawing on stale context from past sessions.

Fix: weekly memory audits. Review what agents actually wrote down. Prune aggressively. Intentional compression beats automated recall every time.

I wrote up the full framework (including brief formats that prevent your #1 failure mode) here if useful: https://bleavens-hue.github.io/ai-agent-playbook/