continuous-agent-loop

Autonomous agent loops require structured orchestration with built-in quality gates and evaluation points to measure progress. Implementing deterministic loop flow with explicit failure-recovery mechanisms prevents drift and ensures workflows remain recoverable across development and production environments.

Quality gates in continuous agent orchestration are configurable checkpoint evaluations applied within deterministic loop flows. They integrate with an evaluation harness to audit loop progress, ensuring AI-agent workflows meet acceptance criteria before proceeding to the next iteration.

To set up an autonomous agent loop, configure the continuous-loop pattern to start with RFC decomposition. Integrate code-quality gates and an evaluation harness, applying session persistence to maintain state and ensure iterative progress is measurable throughout execution.

Failure recovery strategies for long-running AI agent loops include freezing the loop, conducting harness audits, reducing scope, and replaying iterations with strict acceptance criteria. These explicit mechanisms allow workflows to recover from failures without losing session persistence.

Continuous loop orchestration applies directly to iterative AI-agent workflows across development, testing, and production environments. Configurable gate checks and session persistence ensure loop progress remains measurable and recoverable regardless of the deployment stage.

You should avoid continuous autonomous loop patterns when workflows lack measurable progress criteria or cannot define explicit acceptance gates. Without configurable quality gates and evaluation harness integration, deterministic loop flow cannot guarantee recovery or prevent drift.