When requirements shift during a proof of concept (PoC), keep the written scope as the authority—not the latest suggestion from an AI agent or stakeholder. Record each proposed change, decide whether to accept, defer, or decline it, then update acceptance checks and give Codex or GitHub Copilot one bounded task at a time.
Set a scope baseline before asking an agent to build
A PoC should test a specific assumption or demonstrate a specific user problem. Write down the smallest working scenario that can do that, so new ideas can be judged against the purpose rather than added by default.
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- Goal: the assumption or user problem the PoC is meant to demonstrate.
- Audience and scenario: who will use it and the particular flow the demonstration must show.
- In scope: the minimum behaviors needed to test the goal.
- Out of scope: work such as production hardening, integrations, roles, scale, or polish that the demonstration does not require.
- Acceptance checks: observable results that let a reviewer decide whether the PoC works.
- Constraints and open decisions: permitted technologies, data and environment assumptions, plus questions that need answers before implementation.
Keep this baseline somewhere you and the agents can consult, such as the repository or a linked issue. Treat it as your project record, not as a claim that either product automatically controls scope.
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Give each tool stable context and a bounded task
Separate durable project conventions from the requirements of the current increment. Repository instructions can explain how the project is structured and how to build, format, lint, and test it. The task prompt or issue should state the current goal, accepted requirement changes, acceptance checks, and non-goals.
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GitHub’s Copilot tutorial recommends checking repository custom instructions for accuracy and describes useful contents such as a project summary, structure, contribution guidance, and technical principles. It also recommends an environment setup file so dependencies are ready for cloud-agent work: GitHub Copilot tutorial. GitHub says its cloud agent works in an ephemeral GitHub Actions-powered environment, where the ability to build and validate changes can improve pull-request quality: GitHub cloud agent overview.
A task prompt can ask the agent to:
- Restate its understanding of the goal and non-goals.
- Identify ambiguities before editing.
- Propose a plan before a change that is more than a small, isolated fix.
- Implement only the accepted increment.
- Run relevant available checks and report the exact commands and results.
- List assumptions, skipped checks, and any new scope suggestions separately from completed work.
This prompt pattern is a practical way to keep work reviewable; it is not a documented automatic scope-control feature.
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Use a change gate when requirements shift
When an agent or stakeholder proposes a new feature or altered behavior, capture the proposal before implementation. Decide explicitly whether it belongs in the PoC, then change the baseline only if you accept it.
| Change record | What to capture |
|---|---|
| Proposal | The feature or behavior being suggested. |
| Source | Who raised it or what prompted it: a stakeholder request, technical finding, or agent suggestion. |
| Reason | The problem it solves or assumption it tests. |
| Impact | Expected effect on the PoC goal, scope, time, complexity, data, or risk. |
| Decision | Accept, defer, or decline, with the decision owner. |
| Baseline update | The revised acceptance check, if the change is accepted. |
After an accepted change, send the agent the revised requirement as a fresh, bounded instruction. For a deferred or declined proposal, record that decision and keep the existing acceptance checks. This ledger is a process recommendation: the vendor documentation reviewed here describes planning and task execution, not an automatic mechanism for resolving product-scope disagreements.
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Plan, implement, and review in small increments
Codex: plan substantial changes first
For a large change, OpenAI’s Codex best-practices guide recommends starting with an implementation plan in Ask mode, then using that plan for follow-up prompts in Code mode: OpenAI Codex best practices. Compare the proposed plan with the current baseline before asking Codex to implement it.
GitHub Copilot: review a plan before agent execution
GitHub’s IDE documentation describes Plan mode as a way to research a task and draft a plan for review before code changes. Agent mode can then carry out an assigned multi-step task, edit files, and run commands. The IDE documentation also describes Ask mode and the distinct purposes of the available modes: GitHub Copilot agent modes and GitHub Copilot Agent mode. For a material requirement change, review the Plan mode output against the updated baseline before assigning implementation in Agent mode.
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Review behavior, not just test status
Work in one accepted slice at a time. After each slice, inspect the diff and compare the behavior with the acceptance checks. Ask for changed files, checks run, results, assumptions, and unresolved issues. Passing tests show that particular checks passed; they do not by themselves prove that the PoC still tests the intended hypothesis.
OpenAI advises reviewing Codex changes and test results before using them: Codex cloud guide. GitHub warns that Copilot agent output may be incorrect, suboptimal, or vulnerable, and says to review and test it before production use: GitHub Copilot coding agent.
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Preserve decisions and work between tasks
Keep the baseline, accepted changes, task descriptions, and validation outcomes in the repository or linked issues. Codex cloud tasks have separate workspaces: a new task does not recover another task’s uncommitted changes, so commit important work and continue an existing task when you need to extend it. OpenAI documents a saved VM-state recovery window of up to seven days after the last start of a turn or task resume; this refers to VM state, not conversation-history retention: Codex cloud guide.
GitHub’s cloud agent works in the repository specified for a task, uses one branch and one pull request per task, and has a maximum session execution time of 59 minutes according to current documentation. Availability depends on plan and organization policy, so confirm current settings before relying on these limits: GitHub Copilot coding agent and Assigning tasks to the GitHub Copilot coding agent.
How Codex and Copilot fit this workflow
The official documentation describes different workflows, not a general ranking of which tool builds better PoCs or handles requirement changes more accurately.
Quick Recap
| Workflow need | Codex documentation | GitHub Copilot documentation |
|---|---|---|
| Plan before edits | OpenAI recommends asking for a plan on large changes, then using it in follow-up Code mode prompts. Source | IDE Plan mode drafts a plan for review; Agent mode performs the assigned multi-step work. Source |
| Continue work | Continue in the original cloud task; a new task does not restore another task’s uncommitted changes. Source | Cloud tasks are repository-scoped; keep decisions in issues and repository instructions and split work into focused tasks. Source |
| Project context | Prepare the environment and repository connection, and verify access and setup for the intended repository. Source | Use accurate repository custom instructions and set up dependencies for cloud work. Source |
| Validation | Review task changes and test results before using them. Source | The agent can run builds and checks in an ephemeral environment, but output still needs human review and testing. Source Source |
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