Agent Orchestration
The coordination layer that manages how multiple agents, tools, and workflows interact to accomplish complex tasks. Agent orchestration handles routing, state management, error recovery, and resource allocation across the agent system.
Agent orchestration is the infrastructure that turns individual agents into a functioning system. An orchestrator decides which agent handles which request, manages shared state, handles failures and retries, enforces resource limits, and ensures the overall workflow progresses toward completion. Think of it as the operating system for your agent fleet.
For teams scaling beyond a single agent, orchestration becomes the critical engineering challenge. Without it, agents conflict over shared resources, failures cascade unpredictably, and costs spiral as agents run unchecked. Good orchestration provides centralized logging and tracing, circuit breakers for failing tools, budget controls per agent and per task, and clear escalation paths. Frameworks like LangGraph, CrewAI, and custom solutions built on message queues all address orchestration. The right choice depends on your scale: simple workflows can use lightweight orchestrators, while complex multi-agent systems may need dedicated infrastructure with persistent state stores and event-driven architectures.
Related Terms
Model Context Protocol (MCP)
An open standard that defines how AI models connect to external tools, data sources, and services through a unified interface. MCP enables agents to dynamically discover and invoke capabilities without hardcoded integrations.
Tool Use
The ability of an AI model to invoke external functions, APIs, or services during a conversation to perform actions beyond text generation. Tool use transforms language models from passive responders into active problem solvers.
Function Calling
A model capability where the AI generates structured JSON arguments for predefined functions rather than free-form text. Function calling provides a reliable bridge between natural language understanding and programmatic execution.
Agentic Workflow
A multi-step process where an AI agent autonomously plans, executes, and iterates on tasks using tools, reasoning, and feedback loops. Agentic workflows go beyond single-turn interactions to accomplish complex goals.
ReAct Pattern
An agent architecture that interleaves Reasoning and Acting steps, where the model thinks about what to do next, takes an action, observes the result, and repeats. ReAct combines chain-of-thought reasoning with tool use in a unified loop.
Chain of Thought
A prompting technique that instructs the model to break down complex problems into sequential reasoning steps before producing a final answer. Chain of thought significantly improves accuracy on math, logic, and multi-step tasks.