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Use one AI agent to turn a small product goal into a reviewed implementation plan, then give a second agent one bounded coding task. Before accepting the result, inspect the code changes and run the project’s relevant checks yourself. This division can make the handoff clearer, but there is no established evidence that using exactly two agents automatically makes SaaS development faster or less bloated.
What the two-agent workflow is for
The goal is not to add agents for their own sake. It is to separate two kinds of work that are easy to blur: deciding what should change and making that change. Agent one helps clarify scope; you approve or revise its plan; agent two implements the approved task. You remain responsible for product decisions, permissions, review, and release.
This is a practical synthesis of documented capabilities, not a proven productivity formula. OpenAI describes Codex CLI as able to inspect a local repository, edit files, and run commands, while GitHub describes agents that can research, plan, code, and review. Those capabilities make a reviewed handoff feasible; they do not show that two agents outperform one in every project.
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1. Define a small user outcome
Write down the user problem, the smallest useful result, constraints, and how you will tell whether the change works. Keep the task tied to one outcome. For example, “Let a signed-in user update their display name and see the new value on their profile” is more actionable than “improve account management.” Include relevant boundaries such as supported behavior, existing conventions, or files that should not be changed.
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2. Ask the planning agent for a bounded proposal
Give the first agent the relevant repository context and ask it to return a concise plan before editing. Request the likely files or components, ordered implementation steps, assumptions, risks, open questions, and a short acceptance checklist. Treat the plan as a proposal, not an instruction to expand the feature.
3. Review and approve the plan
Resolve uncertainties and remove anything that does not serve the user outcome. Reject speculative architecture, unrelated cleanup, and broad refactors unless they are genuinely required. If the agent cannot explain why a step is necessary, ask it to clarify or leave the step out. This approval point matters: errors in early research or planning can flow into later planning and code.
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4. Hand the approved task to the coding agent
Give the second agent the approved plan, relevant context, and the acceptance checks. Ask it to implement only that task, follow existing project conventions, and report what it changed and which commands it ran. Set permissions to fit the work: an agent that only needs to inspect or propose should not receive broader write access by default.
5. Inspect, test, and decide
Review the diff rather than relying on the agent’s explanation. Check that the changes match the approved scope, inspect any commands or generated files, and run the existing relevant tests, linting, or build checks. If a check fails, or the diff exceeds the plan, request a targeted fix or revert the unnecessary change. Then decide whether the result actually meets the product need and is ready to ship. Passing checks is useful evidence, not a substitute for that decision.
Keep the process lean
- Use two roles only when the handoff helps. For a tiny, unambiguous change, one agent session may be enough; there is no universal optimum established by the cited documentation.
- Ask for explicit assumptions. Hidden assumptions are easier to catch in a plan than after they have become code.
- Keep acceptance checks small and observable. They should tell you what to inspect or run, not invite speculative implementation.
- Limit access to the task. Permissions differ by product and workflow. GitHub’s Agentic Workflows documentation describes read-only defaults and controlled safe outputs for that feature; it does not establish the defaults of every coding agent.
- Verify agent claims. A statement that tests passed is not the same as independently checking which tests ran and what they cover.
Choose an agent setup by where work runs and who controls it
The right setup depends on the project, not on an unsupported claim that one model or product is best. OpenAI distinguishes managed Agents API execution for long-running tasks, an Agents SDK for applications that control deployment and runtime integration, and direct Responses API use for application-controlled integration. For an individual founder, Codex CLI documents a local repository workflow; GitHub documents agent capabilities in repository workflows.
| Decision | What to check |
|---|---|
| Execution | Does work run against a local repository, in a managed environment, or in a workflow tied to your code host? |
| State and tools | Who controls the agent’s context, persisted state, and available tools? |
| Integration effort | Are you using an existing CLI or repository feature, or building and maintaining an application integration? |
| Repository and CI fit | Can the setup work with your repository and checks in the way your team needs? |
| Permissions and approval | What can the agent read or change, and where can a human inspect or approve its work? |
| Costs | Check the current charges for the specific services and usage involved; available documentation here does not establish a total cost for a particular project. |
Where GitHub Agentic Workflows fit
GitHub documents Agentic Workflows as a public preview that uses Markdown instructions in GitHub Actions. Its documentation lists Copilot, Claude, Codex, and Gemini as supported agents and identifies GitHub Actions minutes and inference as cost components. Preview status, supported agents, and billing can change, so check the current documentation before adopting it. The documentation does not provide enough information to calculate a project’s total cost.
Because this is a repository workflow, consider its permission model and approval points alongside its convenience. GitHub says these workflows are read-only by default and restrict write actions to declared safe outputs. That is a description of this GitHub feature, not a blanket guarantee about other agent products or configurations.
What the evidence does—and does not—show
A 2026 manuscript by Ante Kapetanovic, Tomislav Duricic, Andro Mercep, and Emanuel Lacic describes a phased workflow for coding agents and reports practitioner observations that errors in upstream research or planning can propagate into later work. It also identifies a lack of effectiveness metrics as an open problem. The manuscript is dated August 31, 2026, while the listed CIKM proceedings dates are in November 2026; treat it as a pre-publication manuscript as of October 9, 2026, not as settled proof of a universal method.
The available sources support the ingredients of this workflow—planning, repository work, and human review—not a controlled finding that exactly two agents reduce development time, cost, or code bloat. The defensible reason to try the handoff is practical: it creates a clear moment to catch scope creep or mistaken assumptions before implementation.
Quick Recap
Sources
- OpenAI: Agents
- OpenAI / ChatGPT Learn: Codex CLI
- GitHub Docs: Concepts for GitHub Copilot agents
- GitHub Docs: About GitHub Agentic Workflows
- Ante Kapetanovic, Tomislav Duricic, Andro Mercep, and Emanuel Lacic: “A Phased Workflow for Operating LLM-Based Coding Agents”
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