continuous-agent-loop

Autonomous agent loops continuously execute development tasks while quality gates validate outputs against predefined metrics before proceeding. They integrate evaluation mechanisms and recovery controls to ensure code meets standards without manual intervention.

Implement a continuous autonomous loop by defining a sequential execution strategy that refactors code, runs tests, and evaluates results against quality gates. Use recovery mechanisms to handle failures and session persistence to maintain state across iterations.

Yes, continuous agent loops support RFC decomposition to break down specifications into actionable tasks and exploratory parallel generation to produce multiple solutions simultaneously. Both strategies integrate with evaluation harnesses to validate outputs.

The best way to prevent cost drift and loop churn is to configure recovery strategies that detect repeated retries and failures. Quality gates and evaluation harnesses halt or redirect the loop when predefined thresholds are exceeded.

A production stack with session persistence is recommended for continuous autonomous development to maintain state across loop iterations. Combining RFC pipelines, quality gates, and evaluation harnesses ensures robust recovery from failures.

Avoid continuous agent loops when tasks lack clear quality metrics for evaluation gates, or when recovery strategies cannot mitigate cost drift. Without defined failure mode management, autonomous loops risk repeated retries and churn.