continuous-agent-loop

Continuous autonomous agent loops require orchestrated execution with quality gates, eval harnesses, and recovery controls to handle failures and restore states safely. This enables long-running AI agents to operate with minimal human supervision and resilient rollback capabilities.

Quality gates in autonomous agent workflows enforce testable progress and guardrail checks before advancing loops. They ensure each iteration meets defined criteria, preventing unchecked errors in long-running AI agent operations and maintaining reliable execution.

An eval harness provides structured evaluation and metrics to assess autonomous agent loop progress. It coordinates with loop execution to measure performance, enabling iterative decision-making and fault-tolerant operation across diverse environments.

Continuous agent loops apply directly to development scenarios involving long-running agent workflows and iterative decision-making. They coordinate components like RFC pipelines, code-quality gates, and session persistence for resilient operation across environments.

Handling failures in autonomous agent loops requires controlled failure handling and state restoration to safe points. Recovery and rollback mechanisms coordinate with quality gates to ensure safe recovery and prevent cascading errors in long-running workflows.

Continuous autonomous agent loops may not suit tasks requiring frequent human intervention or lacking testable progress criteria. Without quality gates and eval harnesses for measurable validation, autonomous recovery and safe rollback cannot be reliably guaranteed.