What is an agent handoff in a multi-agent system?

An agent handoff is the controlled transfer of a task, responsibility, and relevant context from one agent to another in a multi-agent system. A good handoff makes it clear who owns the task next, what information they need, and what result they are expected to return.

Beginner-friendly example: API test generation

Suppose an AI system is generating tests for an orders API.

• Requirements agent: Extracts endpoint rules from the specification.
• Test-generation agent: Creates test cases.
• Validation agent: Checks whether the tests cover the requirements.

The requirements agent finishes its work and hands the task to the test-generation agent.

A structured handoff might look like this:

JSON
{
  "task_id": "orders-get-42",
  "from_agent": "requirements_agent",
  "to_agent": "test_generation_agent",
  "endpoint": "GET /orders/{id}",
  "requirements": {
    "auth_required": true,
    "success_status": 200,
    "not_found_status": 404
  },
  "expected_output": "positive, negative, and boundary test cases"
}

The receiving agent does not need the complete conversation. It only receives the context required to perform its role.

What should a handoff contain?

A useful handoff usually includes:

• Task identity: A stable task or run ID.
• Current owner: Which agent is transferring responsibility.
• Next owner: Which agent should act next.
• Relevant context: Only the information needed for the next task.
• Expected output: What the receiving agent should produce.
• Constraints: Permissions, deadlines, tools, budgets, or environment restrictions.
• Trace information: Correlation IDs so the handoff can be audited.

Handoff vs. ordinary message

An ordinary message may simply share information.

A handoff transfers responsibility.

For example:

• “The endpoint requires authentication.” → information message
• “Use these authentication requirements to generate the test suite.” → handoff

That ownership change is important because the orchestration system needs to know which agent is responsible for the next action.

Handoffs in LangGraph

In LangGraph, a handoff can be implemented by updating shared state and routing execution to another agent node.

Conceptually:

PYTHON
def requirements_agent(state):
    return {
        "next_agent": "test_generation_agent",
        "endpoint_requirements": {
            "auth_required": True,
            "success_status": 200
        }
    }

Conditional routing then sends execution to the selected agent.

The graph should validate that the requested destination is allowed. An agent should not be able to route work to an unauthorized node or gain additional tool permissions simply by naming another agent.

Direct vs. supervisor-mediated handoffs

• Direct handoff: One specialist transfers work directly to another.
• Supervisor-mediated handoff: A coordinator receives the result and decides which specialist should act next.

Direct handoffs reduce coordination overhead. Supervisor-mediated handoffs provide stronger centralized control.

Common handoff problems

• Passing too much context.
• Losing important constraints.
• Sending work to the wrong agent.
• Creating circular handoffs.
• Allowing multiple agents to believe they own the same task.
• Failing to record the handoff for debugging.
• Changing tenant or permission scope during the transfer.

Practical safeguards

• Use explicit input and output schemas.
• Record ownership changes.
• Limit the allowed handoff destinations.
• Keep context minimal and task-specific.
• Use stable task and trace IDs.
• Add hop limits to prevent infinite routing loops.
• Preserve tenant and authorization boundaries.
• Validate the receiving agent's result before continuing.

In short

An agent handoff is a structured transfer of task ownership from one agent to another. Reliable handoffs require clear context, explicit responsibility, controlled routing, and strong validation so multi-agent workflows remain understandable and safe.