continuous-agent-loop

Continuous autonomous agent loops are orchestrated by coordinating multi-stage task pipelines with built-in quality gates, eval harnesses, and recovery controls to ensure deterministic progression. This approach applies flow selection and session persistence to manage iterative AI automation cycles.

Autonomous AI agent pipelines churn due to lack of deterministic progression and robust error handling. Implementing continuous agent loops with built-in guardrails, quality gates, and recovery controls prevents loop failures and stops churn in iterative automation scenarios.

The best way to add recovery controls to multi-stage agent orchestration is implementing a continuous agent loop pattern with built-in guardrails. This pattern provides failure-recovery options and quality gates to prevent errors in autonomous AI task pipelines.

Decomposing an RFC into iterative tasks for an autonomous agent involves applying flow selection and RFC decomposition within a continuous agent loop. This process coordinates multi-stage orchestration with session persistence and eval harnesses for robust software automation.

Yes, an eval harness is needed to run autonomous task cycles effectively. Continuous agent loops use eval harnesses alongside quality gates to validate deterministic progression, apply recovery controls, and prevent failures in iterative AI pipelines.