Quick Takeaways
- Multi-agent systems run on delegation: split complex work among specialists, then coordinate their contributions.
- Agent-as-a-tool keeps the coordinator in charge: specialists handle subtasks and return findings for the main agent to combine—ideal for EV purchase research.
- Handoff transfers responsibility: a router sends the request to the right specialist, who takes over and answers—like public-charging support.
- These two patterns scale: combine agents in parallel, pipelines, hierarchies, feedback loops, or peer-to-peer systems.
Delegation, Not Just More Agents
A single AI agent can handle many tasks. As an application grows, however, one agent may struggle to manage research, decisions and follow-up questions. The OpenAI Agents SDK offers two ways to divide that work.
With agent-as-a-tool, a lead agent calls a specialist for a defined task. The specialist returns its findings, and the lead agent remains in charge of the final answer. In the SDK, developers create this link with specialist_agent.as_tool(...).
With a handoff, the first agent routes the request to a specialist and transfers control. The specialist then handles the conversation and gives the final response. Developers set those options with handoffs=[...].
The distinction is simple but important: One pattern gathers expertise; the other passes responsibility.
Two EV Questions, Two Patterns
Consider a family in Zurich weighing an electric vehicle. They drive about 15,000 kilometers a year, have a CHF 45,000 budget and cannot install a home charger. A useful adviser must weigh vehicle choices, public charging and ownership costs.
Agent-as-a-tool fits this task. A lead adviser can ask separate specialists to research each issue, then combine their findings into one recommendation. The family gets a joined-up answer, not three disconnected reports.
Now consider an EV owner who already relies on public charging in Zurich and wants help comparing networks and subscriptions. A support agent can route that request to a charging specialist through a handoff. The specialist takes over.
These examples show how the SDK’s Runner can execute an agent and return its result. Developers can also inspect the run to see which tools an agent called or which specialist took control.
Build Carefully, Then Expand
The two patterns can support larger designs. A coordinator might send separate tasks to specialists and combine their work. A sequence of agents might handle a task in stages. Teams can also create hierarchies or add an evaluator that checks and improves a draft.
Still, more agents do not automatically mean better results. Each handoff or specialist call can add cost, delay and another place for errors. Agents also need clear roles, and their research may require human review, especially when prices or other current facts matter.
For teams with distinct, well-defined tasks, multi-agent systems can make an application more flexible. Start with one agent. Add specialists only when delegation solves a real problem.
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