self-learning

To make agents learn from execution history, you need to capture structured execution traces after task completion, extract actionable insights from those traces, and apply adaptations to agent behavior. This builds a persistent knowledge base that prevents recurring failure patterns across multi-agent workflows.

The best way to capture execution traces from multi-agent tasks is to enable structured trace collection after task completion and run a learning pipeline to generate actionable insights. This process records the interactions across orchestrator, worker, and reviewer workflows for post-task review.

You extract actionable insights from agent execution traces by running a learning pipeline that converts the recorded traces into actionable patterns and recommendations. These insights are then stored in a persistent knowledge base to guide future agent runs.

Yes, you can apply session-local, agent-specific, or system-wide adjustments to agent behavior based on the extracted insights. This allows agents to adapt their behavior dynamically during recurring tasks and across future orchestrator, worker, and reviewer workflows.

Yes, this self-learning approach works with recurring failure patterns in orchestrator, worker, and reviewer workflows. It captures execution traces after task completion and extracts insights that enable agents to adapt and prevent similar failures in future runs.