Agent Handoff
The process of transferring a conversation or task from one agent to another, including relevant context and state. Agent handoffs enable specialized agents to collaborate on complex workflows without losing continuity.
Agent handoff is a critical pattern in multi-agent systems and customer-facing AI applications. When a general-purpose agent encounters a task outside its specialization, it packages the conversation context, relevant state, and a summary of what has been accomplished, then transfers control to a more appropriate agent. The receiving agent picks up seamlessly without requiring the user to repeat information.
For product teams building AI-powered support or sales systems, handoff quality directly impacts user experience. A poorly executed handoff that loses context or requires the user to re-explain their problem is worse than no handoff at all. Design your handoff protocol to include structured summaries (not just raw conversation history), clear transfer reasons, and fallback paths if the target agent is unavailable. OpenAI's Swarm framework and Anthropic's agent patterns both provide handoff primitives. Test handoffs extensively, as they are one of the most common failure points in multi-agent deployments.
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.