Run a team AI coding session like a small, reviewable engineering task: agree on one outcome, assign clear roles, constrain the agent’s access, preserve the conversation and decisions, then have someone other than the operator review both the code and the run that produced it. A shared live session and a solo session handed off as a pull request are different collaboration models; choose based on how much real-time steering and shared context the task needs.
Choose the session’s goal and scope
Before opening an agent, decide what the team needs from the session: learning, exploration, a prototype, validation, or community-building. Prepare the project, data, environment, and access in advance so the session is spent on the work rather than setup. OpenAI Academy’s playbook recommends stating objectives and success criteria, protecting build time, and focusing on one meaningful part of a workflow. Its 2026 update suggests teams of three to six for its AI hackathon context; that is a planning recommendation, not a measured optimum for engineering teams generally. See the OpenAI Academy AI hackathon playbook.
For the task itself, write down:
- Outcome: What should be learned, changed, or demonstrated?
- Acceptance criteria: What observable result will count as success, and what tests or checks will establish it?
- Boundaries: Which files, systems, data, or actions are out of scope?
- Human decisions: What must a person decide or verify rather than delegate?
Keep the work small enough to complete or meaningfully test in the available session. If a task cannot be bounded, split it into a discovery session and a later implementation task rather than letting the agent expand the scope without a decision.
Pick a collaboration model deliberately
There is no established evidence here that one collaboration arrangement produces better results for every team. Choose by the needs of the task and compare tools on shared context, ability to steer, environment handoff, reviewability, and access controls. A shared workspace with live terminals and app previews is one implementation described by AQ; it is a vendor’s product description, not an independent endorsement. See AQ’s team workflow guides.
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| Model | What teammates share | When it may fit | Planning consideration |
|---|---|---|---|
| Shared live session | People can see the active session and, depending on the tool, steer it while work is underway. | Exploration or prototyping where teammates benefit from discussing output and corrections in real time. | Agree who operates the agent, who observes, and how decisions or scope changes will be recorded. |
| Solo session with pull-request handoff | The operator completes the run, then shares its code and review materials for others to inspect. | Bounded tasks where asynchronous work and conventional code review are sufficient. | Make the prompt, scope-changing corrections, transcript, warnings, and verification results retrievable for reviewers. |
If work is split across multiple agent runs, assign an owner to each and plan how the results will be reviewed and integrated. Parallel runs do not remove the need for a human to reconcile overlapping changes or inconsistent assumptions.
Set roles and access before work begins
At minimum, identify an operator and a reviewer. In a shared session, also make explicit who is watching the output and who can intervene; role clarity matters even if one person temporarily performs more than one role. For a pull-request handoff, do not make the agent’s operator the only reviewer.
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For Codex deployments, OpenAI describes sandbox controls that define where the agent can write, whether it can access the network, and which paths remain protected. An approval policy determines when the agent must ask before acting outside those boundaries. Set those controls to the project’s risk and access needs before the session rather than treating approvals as an afterthought. OpenAI describes its goal as keeping the agent within clear technical boundaries while making higher-risk actions explicit in “Running Codex safely at OpenAI”. This is an account of OpenAI’s own deployment, not a universal configuration prescription.
Decide what needs to be retained for later review. OpenAI says its Codex deployment includes telemetry for prompts, tool activity, approvals, and network decisions. Logging capabilities and policies vary by deployment; confirm what your own tool records, who can access it, and how long it is retained.
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Run the session so later review is possible
- Restate the task and success criteria. Keep the original brief intact so reviewers can see the initial request and its boundaries.
- Work in a protected block of time. Keep the agent focused on the agreed slice of the workflow; pause to make a human decision if it reaches an out-of-scope choice.
- Record meaningful corrections. Preserve follow-ups that change scope or clarify expected behavior, rather than relying on an operator’s memory of the conversation.
- Note warnings and decisions. Capture warnings the operator chose to pass, approval requests, and choices that may affect safety or behavior.
- Verify the result in its intended context. Record what the operator actually ran or inspected and what remains untested; do not describe generated code as verified if it was not run.
These records help a reviewer understand not only what changed, but how the session reached that change. They also make it easier to reproduce a useful result or diagnose an unsuccessful approach.
Review the run, not only the diff
AQ’s review guide recommends evaluating the process that produced the code as well as the code itself. Its five review layers are the original brief, corrections, paths tried and abandoned, warnings the operator passed, and behavior of the running result. Treat this as practical guidance from AQ, not as an independent standard. Its guide is titled “How to review an AI coding session, not just the diff”.
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- Brief and corrections: Did the agent stay within the original task, and did any later instruction materially change the scope?
- Attempts and abandoned paths: Are there failed approaches or partial changes that affect the final result or reveal an unresolved issue?
- Warnings and approvals: What did the operator accept, bypass, or leave unresolved?
- Running behavior: What did someone actually run or observe, and what remains unverified?
- Diff and tests: Does the code meet the acceptance criteria, and are relevant checks documented?
Automated diff review can assist with code-level inspection, but AQ argues it cannot inspect session context or running behavior. That limitation is specific to the distinction its guide makes; do not assume that any automated review tool covers the full session history unless the tool actually receives and evaluates it. Make another person the default reviewer for agent-authored pull requests, with the materials above available to them.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Close with a decision and an owner
At the end of the session, document what was built, what was learned, limitations or blockers, and the next step with a named owner. Decide whether the result should be tested further, considered for a limited pilot, reused, or stopped. A prototype is a learning artifact, not automatically a commitment to ship. OpenAI Academy’s playbook suggests judging prototypes by relevance, user value, feasibility, usability, human review, repeatability, and learning; it supplies qualitative criteria rather than comparative effect sizes.
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What the available adoption figures do—and do not—show
AQ’s July 29, 2026 review guide reports a July 2026 LeadDev analysis covering 25,264 agent-generated pull requests across 2,361 popular GitHub repositories. In AQ’s account of that analysis, 79 percent reportedly had the same developer review and modify the contribution, and about one in eight workflows involved multiple humans. These are secondary-reported figures, not independently verified here. They describe reported patterns in that analysis; they do not establish which team workflow is safest or most effective.
The evidence available does not establish the best collaboration mode for a particular team, repository sensitivity, regulatory setting, or budget. Use the task’s need for live coordination, the quality of the handoff record, and the deployment’s access and logging controls to make that choice.
Quick Recap
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