Pairon · private beta
AI pair programming with your team
A practical AI pair programming workflow for two developers: share the problem, contribute constraints, guide the coding agent, and review the result.
By Pairon · Updated
AI pair programming works best when people own the goal and review, while a coding agent handles a bounded part of the work. In a shared session, one developer can explain the failure and another can supply the project constraint that a private chat would miss.
Give each person a concrete job
For the first task, choose a driver to write the initial instruction and a navigator to question assumptions. The agent can investigate or implement, but the people decide whether the result fits the problem.
Rotate the human roles after a task. A shared transcript makes it easier for the next driver to see why a decision was made instead of receiving only a recap.
- Driver: state the intended change and choose the agent’s lane.
- Navigator: contribute edge cases, constraints, and review questions.
- Agent: inspect the code, propose a change, and report verification.
A repeatable pairing workflow
In Pairon, connect a computer with the project checkout and start a conversation. Invite the second person as a Collaborator if they need to steer the work; a Viewer is sufficient for observing. Begin with one lane so there is one active implementation to assess.
- Agree on one observable outcome, such as a failing check that should pass.
- Ask the agent to inspect the relevant code and explain its plan.
- Have the navigator add constraints before the edit begins.
- Let the agent make a bounded change, then read the actual diff.
- Run the checks that cover the failure and one nearby regression risk.
- Record the decision and remaining work before starting the next task.
Make the instruction easy to review
A good instruction names the problem, the evidence, and the boundary. Give the navigator a natural point to contribute instead of asking an agent to rewrite a whole feature at once.
Investigate why this request is retried twice after a timeout. First explain the retry path and propose the smallest fix. Keep the public API unchanged. My teammate will add the idempotency requirement before implementation. After editing, show the diff and the relevant verification results.
Illustrative instruction; adapt it to your project.
Review the reasoning as well as the code
Check whether the agent solved the reproduced failure, preserved the agreed boundary, and actually ran the checks it reports. A plausible explanation is useful, but it is not evidence that the change works.
Screen sharing is enough when one person should drive everything. A shared agent room is useful when both people should contribute instructions and decisions. Neither arrangement removes the need for code review.
When the next task becomes independent, move it to a separate lane with its own scope. Keep a dependency in sequence if the second change needs the first result.
Common questions
How is AI pair programming different from autocomplete?
Autocomplete suggests code where a person is editing. An agent-based pairing workflow can include investigation, planning, tool use, edits and verification. The human pair remains responsible for deciding what to change and reviewing the outcome.
Can a reviewer watch without controlling the agent?
Yes. Pairon’s Viewer role can read the conversation. A Collaborator can send instructions and approve requests within their scope, subject to local computer permissions.
Sources and product details
Written by Pairon from the product behavior described below. Examples illustrate a workflow; they are not customer results.