---
title: "Agent Handoff"
description: "An agent handoff is the transfer of control from one AI agent to another during an agentic workflow. It is used when a specialist agent is better suited to continue the task than the current agent."
canonical_url: "https://promptmetheus.com/resources/llm-knowledge-base/agent-handoff"
---

> Index of every page on this site: [llms.txt](https://promptmetheus.com/llms.txt)

An agent handoff is the transfer of control from one [AI agent](https://promptmetheus.com/resources/llm-knowledge-base/ai-agent) to another during an [agentic workflow](https://promptmetheus.com/resources/llm-knowledge-base/agentic-workflow). It is used when a specialist agent is better suited to continue the task than the current agent.

For example, a triage agent may hand off a billing question to a billing agent, a code issue to a [coding agent](https://promptmetheus.com/resources/llm-knowledge-base/coding-agent-agentic-coding), or a workspace-heavy task to an [agent sandbox](https://promptmetheus.com/resources/llm-knowledge-base/agent-sandbox).

An agent handoff normally defines:

- the destination agent;
- a description of when the handoff should be used;
- optional structured metadata such as reason, priority, or language;
- an input filter that controls what history the receiving agent sees;
- approval or guardrail behavior; and
- tracing metadata for debugging.

Handoff is different from calling a [sub-agent](https://promptmetheus.com/resources/llm-knowledge-base/sub-agent) as a tool. In a handoff, the receiving agent becomes responsible for the next turn of the same top-level run. In an agent-as-tool pattern, the outer agent remains in control and the specialist executes as a nested operation.

The main risk is context leakage or context loss. Passing the full conversation may expose irrelevant or sensitive information. Passing too little may omit constraints needed for correctness. [Context engineering](https://promptmetheus.com/resources/llm-knowledge-base/context-engineering) and explicit input filters are therefore important.

Handoffs should be evaluated with realistic routing cases. A handoff description that sounds clear to a developer may still be ambiguous to the model. Incorrect routing can silently degrade quality, increase cost, or bypass needed controls.

The [OpenAI Agents SDK handoffs documentation](https://openai.github.io/openai-agents-python/handoffs/) describes handoff descriptions, structured handoff input, input filters, and history management.

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