A central AI Orchestrator breaks down high-level objectives into granular subtasks, delegating each task to specialized worker agents with defined tool access and roles.

The Orchestrator catches the sub-agent failure, analyzes the error trace, re-assigns the task with corrected context, or routes execution to a secondary agent strategy.

Yes. Agents read and write to a shared memory buffer, allowing research, sales, support, and compliance agents to build on each other's outputs in real time.

Independent subtasks execute in parallel whenever possible, and intermediate states are passed as concise structured JSON payloads rather than full context re-hydration.

Worker agents execute local subtask logic within strict API boundaries, while the Orchestrator retains high-level goal verification, state synthesis, and human escalation authority.