ai-agent-orchestrator

Multi-agent orchestration coordinates autonomous AI agents to execute complex tasks through defined roles, handoffs, and shared context. It enables end-to-end automation workflows such as research, planning, writing, and evaluation across multi-agent collaboration scenarios.

Agent-to-agent handoffs are configured within workflow definitions that establish clear transitions between specialized agents. The orchestrator manages these handoffs alongside shared memory and event communication to maintain continuity across the automation pipeline.

Yes, you can use this multi-agent orchestration framework for research and planning automation. It supports end-to-end automation workflows by allowing you to define specialized agents, assign tasks, and manage dependencies across sequential or complex collaborative processes.

Managing state across autonomous AI agents requires a shared memory module within an orchestration framework. This persists and shares context across agents, enabling continuity and coordinating complex automation tasks effectively.

You handle errors and iteration limits by configuring the orchestrator's supervision capabilities within your workflow definitions. The framework explicitly supports handoffs, error handling, and iteration limits to manage dependencies and prevent infinite loops during task execution.

Yes, building multi-agent automation workflows requires a modular agent framework containing a base agent, specialized agents, orchestrators, shared memory, event communication, and workflow definitions to properly support handoffs, error handling, and iteration limits.