continuous-agent-loop

Build continuous autonomous agent loops by automating planning, execution, evaluation, and recovery stages. This approach handles long-running, multi-session AI tasks with persistent state, parallel exploration, and robust fault recovery.

Quality gates in autonomous agent orchestration are pluggable evaluation harnesses embedded in the loop. They evaluate task outputs at specific stages to ensure reliability before proceeding to subsequent execution or recovery phases.

Maintain session persistence across multi-session AI tasks using modular loop components that save state continuity. This enables long-running tasks to resume seamlessly after interruptions or recovery events.

Add fault recovery to autonomous agent workflows through modular recovery strategies embedded in the loop. These strategies detect failures and trigger persistent state restoration to ensure continuous task reliability.

Yes, you can run parallel exploration within a continuous agent loop. The orchestration framework supports parallel task exploration alongside persistent state and modular loop components for comprehensive multi-session execution.

Avoid continuous agent loop orchestration for short-lived, single-session tasks that do not require persistent state, parallel exploration, or complex fault recovery, as the modular stages and quality gates add unnecessary overhead.