continuous-agent-loop

A continuous agent loop automates decision pipelines by cycling through defined steps, applying quality gates, running evals, and triggering recovery actions on failure. It uses sequential, RFC-based, or exploratory patterns to maintain stable autonomous operation across environments.

Effective workflow orchestration patterns for autonomous agents include sequential, RFC-based, and exploratory loops. These patterns integrate quality gates, eval harnesses, and session persistence to manage decision pipelines and evaluation cycles across development, testing, and production environments.

Add quality gates and evaluations to an AI agent workflow by defining gating criteria within a continuous loop. The loop runs an eval harness at specific steps to validate outputs before proceeding, ensuring autonomous agents meet quality standards in testing and production.

Handle failure recovery in continuous autonomous agent loops by defining specific recovery actions and protocols triggered when quality gates fail. These strategies maintain stable agent operation by automatically correcting faults within decision pipelines and evaluation cycles.

Yes, you need clearly defined loop steps, gating criteria, and recovery actions to automate an agent loop. These modular requirements ensure the autonomous agent workflow integrates with typical agent ecosystems and maintains stable operation across environments.