What is Delegation?
Also called agent delegation, task assignment.
AI agent delegation is a coordination pattern in which a lead agent gives a bounded subtask to a specialist agent, keeps responsibility for the final outcome, and verifies the returned result. A complete delegation states the goal, relevant context, constraints, expected output, and completion check. It differs from a handoff, where responsibility moves to the receiving agent or person.
A delegation can be implemented as a callable specialist, a supervisor that selects the next specialist, or a task queue that workers claim. In every design, the lead retains the overall goal while the specialist receives only the information it needs for one bounded piece of work. This keeps each specialist's context focused and makes its result easier to inspect.
The brief determines the quality of the result. It should name the objective, the relevant facts, constraints, the required output format, and what counts as complete. A specialist cannot recover requirements it was never sent, so vague briefs create work based on assumptions the lead never intended.
The lead must check the returned work before using it. A result can be fluent, well formatted, and still wrong or incomplete. Verification can mean comparing facts with a source, checking a structured output against a schema, running a test, or asking a second specialist to review a high-risk claim.
Delegation pays off when a subtask is separable and its brief is compact. Keep tightly coupled work in one agent when it needs constant back and forth. A supervisor agent is the role that decides who to delegate to, task decomposition creates the units of work, and orchestration infrastructure records and reliably executes the plan.
Key points
- The delegating agent keeps accountability and checks the result.
- A useful brief includes the goal, context, constraints, output, and completion check.
- The specialist receives a bounded subtask, not the entire working context.
- Keep work in one agent when the pieces need constant back and forth.
In practice
A research lead needs current pricing for four competitors. It delegates one site to each research specialist with the same brief: return the plan names, listed prices, source URL, and retrieval date in a short table. The lead checks each table against the cited page, resolves any mismatch, and then writes the comparison. The lead owns the final recommendation even though specialists gathered the evidence.
Delegation FAQs
How does AI agent delegation work?
A lead agent breaks a goal into a bounded subtask, sends the right brief to a specialist, receives the result, verifies it, and integrates it into the final outcome. The lead remains accountable throughout the process.
What should an AI agent delegation include?
A good delegation includes the objective, relevant context, constraints, expected output format, deadline or stop condition, and a clear completion check. These details let the specialist work independently without inventing requirements.
What is the difference between delegation and a handoff?
In delegation, the original agent keeps responsibility, receives the result back, and checks it. In a handoff, responsibility for the task or conversation moves to the receiving agent or person.
When should an AI system delegate work?
Delegate when a subtask is clearly separable, can be briefed compactly, and benefits from specialist tools, knowledge, or parallel work. Keep tightly coupled work with one agent when repeated coordination would cost more than the split saves.