continuous-agent-loop

To build an autonomous agent workflow with automatic recovery, you orchestrate continuous agent loops equipped with fault tolerance controls, eval harnesses, and graceful degradation mechanisms that handle errors during long-running tasks.

Quality gates in continuous agent loops are enforced checkpoints that validate code and task outputs during long-running pipelines. You need them for production deployments requiring robust error handling and reliable multi-step task execution.

You implement fault handling for multi-step AI agent pipelines by applying a production pattern with explicit recovery mechanisms, session persistence, and a structured loop selection flow to route behaviors into appropriate loop variants.

Yes, the continuous-agent-loop pattern supports RFC decomposition for long-running tasks. It combines this decomposition with code quality gates, an eval harness, and session persistence to ensure reliable autonomous agent execution.

The best way to prevent autonomous agents from failing during unattended production deployments is to govern their execution with continuous loops that provide self-healing capabilities, structured recovery controls, and built-in quality gates.

Long-running AI agent workflows lose progress after errors when they lack session persistence and explicit recovery mechanisms. Applying a continuous loop pattern with fault tolerance and graceful degradation preserves state and enables self-healing.