Agent Handoff
Definition
Agent handoff is the process of transferring control from one AI agent to another, including passing relevant context and state so the receiving agent can continue the task seamlessly.
Why It Matters
Effective handoffs are crucial for multi-agent systems. Poor handoffs lead to lost context, repeated questions to users, and failed tasks. Good handoffs ensure continuity - the receiving agent has all the information it needs to pick up exactly where the previous agent left off, creating a seamless experience.
How It Works
A handoff typically includes: (1) a trigger condition (when to transfer), (2) context transfer (what information to pass), (3) state serialization (preserving conversation history and intermediate results), and (4) acknowledgment (confirming the receiving agent is ready). OpenAI’s Swarm framework popularized a lightweight approach where agents can return a handoff to another agent along with relevant context.
When to Use It
Implement handoffs when: (1) different agents specialize in different parts of a workflow, (2) a conversation needs to escalate from a general agent to a specialist, (3) you’re implementing a customer service system with tier-based support, or (4) tasks naturally divide into phases handled by different agents. Keep handoffs minimal - excessive hand-offs create overhead and potential failure points.