autonomous-loop-patterns

To stop autonomous AI agents from running unbounded, you implement explicit loop control architecture with trigger, worker, progress signal, stop condition, durable state, and safety cap primitives. This turns iterative execution into a checkable design with stall detection.

A checkpoint-resume loop is a pattern for multi-session agent jobs that persists durable state contracts outside model memory. It requires defining a stop condition, durable state schema, and enforced safety caps so progress remains verifiable across sessions without silent failures.

You design safety caps and stall detection for agent ops by enforcing explicit limits on iterations, errors, time, and budget. The architecture must define explicit completion and blocked states, generating verifiable progress evidence to detect when the worker stalls.

You need an external supervisor respawn system for autonomous loops when progress must be verifiable in long-running or multi-session tasks. The supervisor monitors sentinel continuation workflows and respawns workers while enforcing stop or blocked semantics outside the agent.

Bounded single-run loops execute a task once within strict safety caps, while sentinel continuation workflows keep executing iteratively until a specific stop condition is met. Both require explicit progress signals and durable state contracts to prevent silent failures.

Yes, you can apply this loop control design without external dependencies. It provides pattern selection for common loop types and specifies durable progress contracts, requiring only that you enforce safety caps and stop or blocked semantics outside the worker model.