continuous-agent-loop

Autonomous agent loops are kept stable by implementing a deterministic continuous loop with quality gates, evals, and recovery options. This structured control ensures progress remains measurable and prevents workflow drift during long-running execution.

Recovery strategies for autonomous agent loops include freeze, audit, and replay mechanisms. These defined failure mode controls allow you to halt execution, investigate anomalies, and resume workflows safely without losing progress.

You integrate evaluation steps by applying a continuous loop with an integrated eval harness and quality gates. This setup provides ongoing validation throughout execution, ensuring AI workflows remain stable and meet quality benchmarks before proceeding.

Yes, continuous agent loops are applicable to long-running planning and execution tasks across RFC-style pipelines. The loop selection flow routes execution paths specifically for RFC decomposition, parallel exploration, and quality-driven deployments.

The best way to manage persistent sessions in autonomous AI workflows is using production-stack guidance with continuous loops. This approach combines quality gates, eval loops, and recovery controls to maintain stable, measurable progress across long-running tasks.