AI coding agent orchestration: roadmap to pull request

Learn how to orchestrate AI coding agents through task planning, context handoffs, implementation, independent review, and recovery, with a worked example.

AI coding agent orchestration is the coordination of planning, task dependencies, implementation, and review around agents that change a codebase. It determines what each agent should do, what context it receives, and how its result becomes work an engineer can assess.

A useful workflow keeps the reason for a change attached to the work. The implementer needs to know what should happen, the reviewer needs a way to check it, and the engineer merging the pull request needs to understand what remains uncertain.

What needs orchestration beyond a coding session?

A coding agent can inspect a repository, implement a change, and run checks within a session. As the work grows, someone still has to connect that session to the product goal, coordinate dependent changes, and decide what should happen after a failed attempt or a review finding.

Several agents running at once do not answer these questions by themselves. A small change may need one implementer and one separate review. Additional sessions make sense when the work has independent responsibilities and each result has a clear consumer.

A worked example: export project activity

Consider a product team that wants customers to download a project’s activity as a CSV file. This is a worked example, not a report of a customer deployment. It follows the roadmap, initiative, backlog, implementation, and review flow presented on the Ad Actum product page.

Start with the outcome

The roadmap priority is to make project activity easier to share. The first initiative is deliberately small: let an authorized project member export the activity they can already see, using the same date filter as the activity screen.

That definition gives the work a boundary. Scheduled exports, email delivery, and organization-wide reporting can wait. They are different capabilities, not prerequisites for a useful first export.

Write down an observable result: a member selects a date range, downloads a file, and sees the same eligible records as in the activity view. Someone without access to the project cannot export it.

Give each task the context it needs

The backlog can describe three connected responsibilities:

Reference actual files and existing contracts once the repository has been inspected. A prompt that says “follow the usual permissions” is incomplete if the receiving agent cannot locate those permissions.

Coordinate implementation around dependencies

Agree on the request and response before building the download action. Work can proceed independently where the contract is stable; two agents editing the same query at once need coordination.

The handoff should carry the implemented behavior, relevant checks, and unresolved questions. If the backend changes the meaning of the date filter, that belongs in the handoff to the UI and reviewer. A successful agent session alone does not establish that the feature works.

For this example, a local fixture with two projects and dated activity records makes the result easy to inspect. It avoids needing customer data to demonstrate the workflow.

Review the feature through its normal entry point

Follow the download from the button to the data query and back to the file. Try an empty range, a range with records, and a user without access. Check how the UI responds when the request fails. Establish a size limit or a background export path if the expected data size requires one.

The review should produce concrete corrections where behavior differs from the agreed outcome. It should also record the limit of the evidence: a small fixture cannot establish performance on a large production dataset.

Keep implementation and approval separate. Give the reviewer the intended behavior, the resulting code, and the available checks. Ask it to follow the feature from its normal entry point, rather than treating the implementer’s account of the change as proof that it works. An unresolved access-control defect remains a defect even if the agent session completed successfully.

Recover an interrupted run without losing the work

Suppose the endpoint and its local checks are complete, but the UI session stops before wiring the download button. Preserve the code changes and record which result was checked. The next session needs that state, the remaining UI responsibility, and any changed request or response contract.

Re-running the whole feature from the original prompt can duplicate work or overwrite a valid correction. Resuming also needs care: inspect the current repository before assuming that the earlier endpoint is still present or that its checks still describe the current code.

For this example, recovery is complete when the next agent can finish the button, exercise the same export scenario, and carry any unresolved findings into review. A log saying that the previous session ended is insufficient.

Make the pull request easy to assess

Describe what the user can now do, which behavior was checked, and any material limit. Include the evidence needed to repeat the relevant scenario. The engineering team still decides whether to merge.

This continuity is the purpose of coding agent orchestration: business intent survives the transitions between planning, implementation, and review. Ad Actum is exploring this workflow with private beta teams; capabilities and deployment are agreed for each pilot.

For a closer look at one review concern, read our worked example of cross-tenant authorization.