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Choose the session’s goal and boundaries
Start by naming what the session should accomplish: learning, exploration, a prototype, validation, or community-building. Prepare the relevant project, data, environment, and access in advance. OpenAI Academy’s 2026 playbook suggests teams of three to six for its AI hackathon context, describing that size as large enough to bring different perspectives while remaining manageable. It is a planning suggestion, not a measured optimum for engineering teams generally. OpenAI Academy’s AI in the Workplace Hackathon Playbook also advises teams to state objectives and success criteria, protect build time, and focus on one meaningful part of a workflow.
For the coding task itself, write down:
- The requested change and what is out of scope.
- Acceptance criteria, including the tests or observable behavior that would count as success.
- Which decisions require human judgment rather than agent action.
- Who will operate the session, who will observe or test, and who will review the result.
A task that is small enough to complete or meaningfully test is easier to steer and assess than an open-ended request to “improve” a project.
Pick a collaboration model that fits the work
Two common patterns are a shared live session and a solo session followed by a handoff. A shared session gives multiple teammates visibility and an opportunity to steer while work is underway. In a handoff model, one person runs the agent and presents a diff or pull request for others to review. Tool capabilities vary, so choose based on the context, intervention, handoff, review, and access needs of the task rather than assuming one model is always better.
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| Decision axis | Shared live session | Solo run with handoff |
|---|---|---|
| Shared context | Teammates can follow the active session, depending on the tool. | Teammates typically receive the completed diff or pull request; access to the prompt and transcript must be arranged. |
| Ability to steer | Teammates may be able to intervene while the agent works. | Feedback usually comes after the operator has run the task. |
| Environment handoff | A shared workspace may let teammates inherit the environment and its history. | The operator may need to provide instructions, transcript, setup details, and a reproducible result. |
| Reviewability | Useful only if the brief, changes in direction, warnings, and result remain retrievable. | A pull request supports code review, but context and running behavior need to be supplied separately. |
| Access and governance | Define who can steer and what the shared environment can access. | Define the operator’s permissions and ensure reviewers can see enough evidence to assess the change. |
These are practical comparison criteria synthesized from AQ’s team workflow guides, its guide to reviewing an AI coding session, and OpenAI’s description of Codex controls. AQ describes its own shared workspace, including live terminals and app previews; that is a vendor’s account of its product, not independent evidence that a particular arrangement improves outcomes.
If you run multiple sessions in parallel, assign an owner to each and decide how the results will be reviewed and integrated. Parallel work without an explicit integration step can leave the team with overlapping changes and no clear reviewer.
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Set access and approval rules before work begins
Agree on what the agent may read or change, whether it may use the network, which paths must remain protected, and which actions need human approval. In its description of Codex deployment, OpenAI says the sandbox determines where Codex can write, whether it can access the network, and which paths are protected; approval policy determines when it must ask before acting outside those boundaries. Its stated aim is to keep the agent within technical limits while allowing low-risk work to proceed and making higher-risk actions explicit. OpenAI’s account of running Codex safely also describes telemetry for prompts, tool activity, approvals, and network decisions.
Those controls describe OpenAI’s Codex deployment, not a universal feature set or policy prescription for every coding agent. Teams should set boundaries that match their own repository sensitivity, deployment, and obligations, and verify that the chosen tool actually supports the controls they need.
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Agree who is driving and who is watching. Keep the scope small, protect time for the work, and make human verification visible. If a follow-up instruction changes the task, preserve it; a reviewer needs to distinguish the original request from later decisions. Also capture warnings, failed or abandoned approaches, and any choice made to proceed despite a concern.
For a consequential change, do not treat a successful tool run as proof that the behavior is correct. Identify the relevant tests or manual checks in advance, and have a person run or inspect them. The operator should report what they personally verified rather than implying that an agent’s completion message is a test result.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Review the run as well as the code
A diff shows what changed, but not necessarily why it changed or what happened along the way. AQ’s review guide frames session review around five layers: the brief, corrections, paths tried and abandoned, warnings the operator passed, and behavior of the running result. It argues that automated diff review cannot by itself inspect session context or running behavior; treat that as AQ’s guidance, not a comparative evaluation of every review tool.
Before handing off a pull request, make the following retrievable:
- The original task instructions and acceptance criteria.
- Follow-up instructions that changed scope or direction.
- A transcript or other record of the session, including important attempts and warnings.
- The operator’s account of what was run or checked and what remains unverified.
- The diff and, where relevant, a way for the reviewer to examine the running result.
Make a second person the default reviewer for agent-produced pull requests. The operator’s familiarity with the session is useful, but it is not a substitute for another person examining the result and its context.
Close with a decision and an owner
Record what was built, what the team learned, limitations or blockers, and the next step with a named owner. A prototype can be a learning example, need further testing, be suitable for a limited pilot, be reusable, or be stopped. OpenAI Academy’s playbook cautions against treating every prototype as a commitment and suggests evaluating relevance, user value, feasibility, usability, human review, repeatability, and learning. It supplies qualitative criteria rather than comparative effect sizes.
For claims about team review practices, keep the evidence in perspective. AQ’s July 29, 2026 guide reports LeadDev analysis of 25,264 agent-generated pull requests across 2,361 popular GitHub repositories: it says 79 percent had the same developer review and modify the contribution, and about one in eight workflows involved multiple humans. AQ is reporting those figures secondhand; they describe the analyzed workflows, not a universal rate or proof that a given collaboration model is better.
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