The Tool Desk
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What a two-agent chain should do
In a simple chain, Agent 1 produces an intermediate result and Agent 2 uses it to complete a distinct task. For example, Agent 1 could extract key facts from a request, while Agent 2 turns those facts into a concise response. The second task should depend on the first output; otherwise, the agents are simply doing separate work.
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Before connecting the agents, define three things: what Agent 1 is responsible for, what format its output must follow, and what Agent 2 should do with that output. Also decide what happens when the first result is missing, ambiguous, or unsuitable. These are workflow design choices; the OpenAI Agents SDK documentation does not prescribe a spreadsheet schema or guarantee that a chain will be reliable just because it uses multiple agents.
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The following columns are an illustrative template for making each stage inspectable. They are not an SDK standard. A spreadsheet may serve as a record, an interface, or both, but only if the actual implementation connects it to the workflow.
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| Column | What to record | Illustrative prompt or use |
|---|---|---|
| Task ID | A unique identifier for the row or task | Use an ID that lets a reviewer find the related work. |
| Original request | The user’s request or the approved task description | Keep the request distinct from the agents’ instructions. |
| Agent 1 assignment | The bounded first-stage task | “Extract the key facts needed for a response. Do not draft the response.” |
| Agent 1 output | The intermediate result in the agreed format | For example, a short list of facts with uncertainty called out. |
| Review/status | Whether the output is ready for the next stage | Use a defined status such as “ready,” “needs review,” or “incomplete.” |
| Agent 2 assignment | The second-stage task and how it should use Agent 1’s result | “Draft a response using the approved facts. Do not add unsupported details.” |
| Agent 2 output | The second agent’s result | Store the draft or other requested deliverable. |
| Final review | Who accepted, changed, or rejected the result | Record a human or manager’s decision before treating the output as final. |
The fields passed forward are usually the task context, Agent 1’s output, and Agent 2’s assignment. The task ID, status, and review fields primarily help a person inspect and manage the work. Your implementation determines which fields agents actually receive.
Choose who owns the final answer
The main design decision is whether the first agent, a manager, or the second agent remains responsible for the user-facing result. OpenAI’s Agents SDK Agent Orchestration guide describes two common patterns:
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Keep a manager responsible with agents as tools
A manager can call specialist agents for bounded subtasks, combine their outputs, and produce the user-facing answer. The specialist contributes work but does not take over the conversation. As the guide puts it: “Use agents as tools when the specialist should help with a subtask but should not take over the user-facing conversation.”
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A handoff routes work to a specialist, which becomes the active agent for the remainder of that turn. The handoff preserves conversation context while narrowing the active instructions. Choose this when the specialist should own the next response or workflow branch, rather than merely supply an input to a manager.
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These are distinct control-flow choices, not different names for putting two outputs on a spreadsheet row. The spreadsheet can display what happened; the orchestrator determines how work moves and who is responsible.
Decide whether the model or your code controls the chain
With code-defined orchestration, the application specifies the flow—for example, it can transform Agent 1’s output into Agent 2’s input and invoke the second stage. The Agents SDK guide describes code orchestration as more deterministic and predictable in speed, cost, and performance than relying on a model to make every orchestration decision. That is a qualitative comparison, not a guarantee of any particular outcome.
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If the model selects tools or routes work, the model has more say over control flow. If application code explicitly advances the task, the sequence is defined by the application. In either case, write down the expected output format and the rule for proceeding, retrying, or sending a result for review.
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Understand where the agent loop and state live
OpenAI’s API documentation distinguishes implementation routes by who runs the agent loop and manages state. The Agents SDK runs in the developer’s application, so deployment, storage, approvals, and runtime integration remain under application control. The Agents API is a managed harness; the Responses API is a lower-level option for direct model calls or a custom agent implementation. A two-agent chain does not require one particular route.
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The cited documentation does not establish spreadsheet compatibility, automatic persistence, or any specific spreadsheet integration. Treat those as implementation details to verify for the tools and permissions you actually use.
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