continuous-agent-loop

You can manage continuous autonomous agent loops by applying quality gates, evals, and recovery controls to detect regressions and minimize downtime. This approach provides explicit loop patterns, failure-mode handling, and persistent sessions for robust multi-agent workflows.

Quality gates are automated checkpoints within an autonomous agent loop that detect stagnation or regressions. They use an eval harness to monitor progress and trigger recovery actions if the workflow stalls or fails to meet standards.

Recovery strategies for stalled autonomous agent loops include freezing the state, executing targeted replays, and reducing scope. These mechanisms minimize downtime and restore reliable operation within your multi-agent workflow.

The best way to orchestrate autonomous agents is using a production-ready stack with an RFC pipeline, code quality gates, an eval harness, and persistent sessions. This infrastructure ensures clear loop selection and robust failure-mode handling.

Yes, continuous agent loops are applicable to building and operating multi-agent workflows across both development and production environments. They provide explicit loop patterns, gates, and persistence mechanisms for reliable execution.

Autonomous agent loops fail during long-running workflows due to undetected stagnation or regressions without quality gates. Implementing evals and recovery controls like targeted replays prevents these failures and maintains reliability.