The software infrastructure that coordinates multiple AI agents, tools, and LLM calls to execute complex multi-step workflows.
The orchestration layer is the middleware that sits between user-facing application code and the underlying AI models and tools. It is responsible for managing the lifecycle of agentic workflows: initializing state, sequencing steps, managing tool calls, handling errors, persisting checkpoints, and delivering results.
Leading orchestration frameworks in 2026 include:
LangGraph: Graph-based orchestration with native support for cyclical workflows, state management, and HITL interruptions. Ideal for complex, stateful multi-step tasks.
CrewAI: Role-based multi-agent orchestration where specialized agents (researcher, writer, critic) collaborate on tasks. Excellent for knowledge work.
PydanticAI: Type-safe agent framework that leverages Python's Pydantic library for validated state management. Preferred in teams with strong typing discipline.
AutoGen: Microsoft's multi-agent conversation framework, enabling agents to delegate tasks to each other through structured message passing.
The choice of orchestration framework is one of the most significant architectural decisions for an agentic site. Key evaluation dimensions include:
- Debugging and observability tooling
- Support for HITL interruption and resumption
- Scalability and deployment model
- Community and ecosystem maturity
- Integration with preferred LLM providers