agent-harness-construction

Autonomous agent loops are built by configuring a supervisor with your agents to run persistent plan-execute-evaluate-adapt cycles. This setup applies self-healing retry policies and safety governance hooks to handle failures and prevent regressions.

Multi-agent orchestration coordinates tasks across agents using supervisor and council patterns to ensure traceable decisions. It applies deterministic task coordination and safety gating to manage complex workflows across multiple autonomous agents.

Safety gating and governance metadata are added to agent pipelines through configurable governance hooks. These hooks apply runtime validation to ensure deterministic task coordination and prevent regressions during autonomous agent execution.

Yes, this multi-agent orchestration harness is designed specifically for complex workflows requiring plan-execute-evaluate-adapt cycles. It applies self-healing retries and measurable performance tracking to drive persistent autonomous workflows.

Yes, multi-agent orchestration supports governance-aware measurement through lightweight telemetry and outcome-trace collection. This provides measurable effectiveness and performance quality tracking for autonomous agent loops.

The best way to prevent regressions when coordinating autonomous agents is applying configurable retry policies and failure handling within governance hooks. These self-healing safety mechanisms ensure robust multi-agent orchestration without workflow regressions.