diagnose-why-work-stopped

To diagnose stalled AI agent workflows, perform a forensic root-cause analysis on the issue tree to trace state transitions and pinpoint exact failure points. This identifies where agent coordination broke down to restore operational liveness.

Infinite recovery loops in agent orchestration occur when execution semantics and invariant-based planning fail to preserve productivity. Analyzing the blocked issue tree traces state transitions to pinpoint exact failure points and establish loop prevention contracts.

Prevent stalled issue trees by converting one-off diagnostic fixes into general product-rule contracts. These contracts preserve productive work, block-only stops, and enforce loop prevention to maintain robust agent coordination without manual code intervention.

Forensic tree analysis works for complex agent coordination by systematically tracing issue blocker chains and state transitions. It provides a diagnostic framework to restore progress through product-level governance, bypassing the need for manual code intervention.

You need product-rule formulation for AI agent state management when an agent team stalls without a clear action path. It converts diagnostic findings into contracts that preserve productivity and block-only stops, ensuring productive work continues without infinite loops.