runaway-detection

To detect stuck agents, you monitor progress timestamps, error counts, and budget thresholds to identify stalling conditions and trigger structured intervention protocols. This systematic detection helps regain control of looping agents before they waste resources.

The best way to stop looping AI agents is using structured intervention workflows that safely halt execution based on detected stagnation patterns. These protocols automatically recover agents and provide escalation guidance to preserve completed work.

You should intervene in an orchestrator environment when progress signals stop updating or error counts exceed defined budget thresholds. Detecting these specific stagnation conditions triggers immediate recovery actions to prevent further resource waste.

Yes, you can use automated monitoring with self-monitoring protocols to recover stuck agents and safely preserve work. The system detects stalling conditions, halts the agent, and provides structured recovery guidance to resume progress.

Yes, runaway-detection works with executor agents by implementing self-monitoring protocols that track progress timestamps and error counts. It applies to both orchestration and execution environments to detect stagnation and trigger necessary interventions.