Use one AI agent to clarify and plan a small product change, then give a second agent the approved plan and a tightly bounded implementation task. You remain responsible for scope, permissions, code review, tests, and the decision to ship. This is a practical workflow, not a proven formula: the available sources do not show that using exactly two agents reliably saves time or prevents bloat.
Why separate planning from implementation?
A coding agent can inspect a repository, edit files, and run local tools; that capability does not make its output automatically correct. Separating planning and implementation creates a review point before code changes begin: you can challenge assumptions, trim unnecessary work, and define what success means.
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GitHub documents agent capabilities that include research, planning, coding, and review, while Codex CLI documentation describes working with a local repository and reviewing changes. Those documented capabilities make a reviewed handoff feasible; they do not establish that a two-agent process is better for every project.
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How to use two agents for one SaaS change
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Define one user outcome
Write down the user problem, the smallest useful result, constraints, and checks that would show the change works. For example, for a password-reset feature, specify the user action and expected confirmation, plus relevant security and error-handling requirements. Avoid bundling unrelated cleanup or speculative architecture work into the same request.
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Ask the first agent for a plan, not code
Give the planning agent the relevant project context and ask it to return the likely files or components, an ordered set of steps, assumptions, risks, and questions that need your decision. Require a short acceptance checklist tied to the user outcome. Codex CLI documents repository inspection and planning-related work; GitHub’s agent concepts describe research and planning among agent tasks. Codex CLI documentation · GitHub Copilot agent concepts.
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Review and narrow the plan yourself
Reject work that does not serve the defined outcome. Resolve consequential assumptions before implementation, and make the plan small enough to review. If the agent proposes a broad refactor, new service, or additional feature, ask whether that work is necessary for the acceptance checks; do not treat a plausible-sounding proposal as a requirement.
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Give the second agent one bounded implementation task
Pass along the approved plan, the acceptance checklist, and only the context needed to make the change. Specify the expected scope and ask the agent to report files changed, commands run, and anything left unresolved. Set repository access and write permissions to fit the task; an agent that only needs to suggest a change should not receive broader authority by default.
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Review the diff and verify the result
Inspect the actual changes rather than relying on the agent’s summary. Compare the diff with the approved plan, examine commands or other actions, and run the project’s relevant existing checks. If a check fails, or the diff exceeds the agreed scope, ask for a targeted correction and review the revised diff. Codex documentation describes inspecting activity and reviewing changes before commit or pull request. Codex CLI documentation.
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Make the release decision
Decide whether the implementation meets the user need and is ready to ship. Agents can help produce and inspect work, but they do not validate the business idea or take responsibility for release.
What keeps the process lean?
- Use two roles only when the handoff helps. For a tiny change, one agent session may be enough. The cited sources do not establish a universal optimum number of agents.
- Make assumptions visible. Ask the planning agent to name uncertainties and questions instead of silently filling gaps with guesses.
- Keep the task tied to one outcome. A narrow feature request is not an invitation to redesign the application.
- Limit permissions to the work. GitHub says its Agentic Workflows feature is read-only by default and restricts write actions to declared safe outputs. That policy describes this GitHub feature, not all coding agents. GitHub Agentic Workflows documentation.
- Verify claims with observable evidence. The diff and project checks matter more than an agent’s assertion that the task is complete.
Which kind of agent setup fits your project?
Choose based on where code runs, who controls state and tools, how the agent connects to the repository, how permissions and approvals work, and what usage costs apply. These are operational trade-offs, not grounds for declaring a universal winner.
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| Approach | Execution and control | When to consider it |
|---|---|---|
| Codex CLI | Documented as a local repository workflow that can inspect files, edit code, and run local tools. See Codex CLI documentation. | When an individual founder wants to work against a local codebase and review changes in that development loop. |
| Managed Agents API execution | OpenAI’s agent guide describes managed execution for long-running tasks. Consult the guide for execution, state, tools, and integration details: OpenAI Agents guide. | When an application needs managed execution rather than a purely local coding loop. |
| Agents SDK | The application controls deployment and runtime integration; integration and state-management choices depend on the application. See the OpenAI Agents guide. | When you need to integrate agent behavior into an application while controlling its runtime. |
| Direct Responses API | The application controls the integration directly. The guide compares this approach with managed execution and the SDK: OpenAI Agents guide. | When you want application-level control over an agent integration and are prepared to build around it. |
| GitHub Agentic Workflows | Markdown instructions run in GitHub Actions; GitHub documents read-only defaults, controlled safe outputs, and separate Actions-minute and inference cost components. Total cost is not stated in the documentation cited here. The feature is documented as a public preview and may change. See GitHub Agentic Workflows documentation. | When repository and CI workflow integration is central and the preview’s current capabilities and billing model suit the project. |
Before committing to a setup, check its current availability, permissions, state handling, integration effort, and billing documentation. In particular, GitHub’s preview status, supported agents, and costs can change; the documentation lists Copilot, Claude, Codex, and Gemini as supported agents for the feature.
What this workflow can and cannot promise
The method gives the founder a deliberate checkpoint between an agent’s interpretation of a task and its implementation. That can make scope and review more explicit, but the sources cited here do not provide controlled evidence that exactly two agents reduce development time, cost, or code bloat. Those outcomes depend on the project and would need to be measured rather than assumed.
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