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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteThere is no established ideal number of “hops” in an AI-agent workflow. Treat a hop as a transfer of control or context, then ask whether each transfer adds useful specialization, who should own the final response, and what information the next agent needs. OpenAI’s guidance distinguishes a handoff, where a specialist takes over, from an agent-as-tool call, where a manager remains responsible for the user-facing answer.
What counts as a hop in an agent workflow?
“Hop” is a useful design metaphor, not a standardized technical metric. Here, it means a transfer between agents or between orchestration logic and an agent. A workflow with several transfers is not automatically worse than a single-agent workflow: the important question is what each transfer accomplishes and what context crosses the boundary.
Vendor documentation describes different orchestration patterns and their trade-offs, but it does not establish a universal optimal handoff count or provide a comparative benchmark. Avoid choosing an arbitrary maximum. Instead, make every transfer justify its added routing and context-management complexity.
Choose the pattern by deciding who owns the next response
The key distinction is whether the specialist takes control of the next user-facing response or returns a bounded result to a manager. OpenAI’s API guide describes the distinction in those terms: Orchestration and handoffs.
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| Pattern | Who controls the final user-facing response? | What the specialist does | Routing style |
|---|---|---|---|
| Handoff | The specialist takes over the next response. | Owns the next part of the interaction. | Can be model-directed; code can also define workflow steps. |
| Agent as a tool | The manager remains in control. | Returns a bounded result for the manager to use. | Can be invoked as part of model-directed orchestration or a code-defined flow. |
| Code-directed sequence | Determined by the application’s orchestration logic. | Performs a step selected by code, which may chain agents, run parallel tasks, or use evaluator loops. | Code decides the sequence rather than leaving the overall flow open-ended. |
These are design distinctions in OpenAI’s documentation, not results from a measured head-to-head comparison. See the OpenAI Agents SDK guide to agent orchestration and the OpenAI API guide.
When to keep work with one agent
Keep a task with one agent when splitting it would add transfers without a clear benefit. A specialist is useful when it has a distinct responsibility; if the same agent can complete the work coherently, extra routing may add coordination rather than capability.
- Keep the work together when there is no clear specialist boundary.
- Split it when a distinct agent can own a meaningful part of the task or return a useful bounded result.
- For open-ended work, model-directed planning can choose the route dynamically; OpenAI describes this as useful when the next step is not fully specified.
When to hand off—and when to call an agent as a tool
Use a handoff when the specialist should take over
A handoff fits when the specialist should own the next response or interaction stage. OpenAI’s Agents SDK documents handoff configuration and optional filtering of the information passed to the receiving agent. Review the OpenAI Agents SDK handoffs guide for the implementation details.
Use an agent-as-tool call when the manager should stay in charge
Call a specialist as a tool when it should perform a bounded task and return a result, while the manager composes or controls the final answer. This pattern preserves the manager’s role rather than handing the next response to the specialist.
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OpenAI describes code-directed orchestration as more deterministic in flow, speed, cost, and performance than leaving orchestration to the model. Those are qualitative design considerations, not guaranteed outcomes or quantified benchmarks. Code is a natural fit when the sequence should be explicitly controlled; model-directed planning is useful when the work is open-ended.
Decide what context crosses each boundary
There is no universal context rule across agent frameworks. In the OpenAI Agents SDK, the receiving agent gets the previous conversation history by default, and the handoff can be configured to filter that input. Anthropic describes its managed agents as working in separate session threads with their own conversation histories. These are vendor-specific implementation descriptions, not properties that can be assumed of every multi-agent system.
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Before adding a transfer, specify what the next agent needs to do its job: relevant user requests, prior decisions, constraints, and any structured result from earlier work. Then check what the framework actually passes, what it omits, and whether the receiving agent can see enough to continue. Passing more history is not automatically better; filtering should preserve task-critical information.
A practical way to count and review hops
- Map the control flow. Write down each agent invocation and mark whether control stays with a manager, passes to a specialist, or is determined by code.
- Name the purpose of each transfer. State what the specialist contributes or why the control boundary is needed. Remove transfers with no distinct purpose.
- Assign response ownership. Decide which agent or application component is responsible for the next user-facing response.
- Define the input contract. Specify what conversation history or structured information reaches the next agent, and configure filtering if the framework supports it.
- Choose model-directed or code-directed routing. Prefer model planning when the route is open-ended; use code when the sequence needs explicit control.
- Review the result by function, not raw count. Ask whether the workflow routes work to the right specialist, preserves necessary context, and keeps response ownership clear. The hop total alone cannot establish quality.
What the documentation does—and does not—establish
The cited OpenAI and Anthropic materials support distinct orchestration and context patterns, but they do not establish a best number of agent handoffs or a universal performance advantage for one architecture. Treat “count the hops” as a prompt to inspect control transfers and information flow—not as a benchmark or a target number.
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