How to coordinate multiple AI coding agents without collisions
Task boundaries, context, worktrees, contracts, and integration: a method for running multiple AI coding agents in parallel without multiplying conflicts.
8 min read
Building a complete application with AI involves more than generating code. The work includes defining the use case, preserving data, enforcing access, handling failures and delivering an environment someone can actually use.
An agent team can handle sequential or independent missions. Evaluate its autonomy through accepted outcomes and the human interventions required to obtain them. This guide provides a method and an illustrative scope, rather than claiming an application was built without intervention.
Define what complete means
An application is complete relative to an objective. A small internal tool can be finished without payments or messaging. A screen demonstration is insufficient when the objective requires accounts, persisted data and several users.
Write the main journey before listing screens. Who starts, what do they do and which state should the next user find? Add the main failure and the way work resumes after interruption.
A member signs in, creates a request and follows its status. A manager can assign it and close it after handling. Requests survive signing out and back in. Members of another team cannot read them. Errors are explicit and an interrupted creation can resume without duplicating a request.
External notifications, payments and a native mobile app are outside this first version. They become separate work if the need is confirmed.
This scope already requires checking UI, API, data and permissions. It provides an observable definition of completion. Use the AI project brief to prepare these inputs.
Prepare the conditions for autonomy
Choose the repository, working branch, development environment, permitted external services and deployment target. Prepare access without placing secrets in project documents. Check available resources and actions that require approval.
Count that preparation in the experience. An application created without manual code may still require substantial configuration and decisions. Preserve those interventions in the log instead of removing them from the final account.
AGENTS.md and CLAUDE.md describe working rules for coding executors. Effective runner permissions determine what they can read, change or launch. Keep both levels consistent.
Break the project into integrable outcomes
Start with shared contracts: data model, authorization and exchange formats. UI and backend built independently around different assumptions create rework.
Prepare cards with verifiable results. “Build the backend” is broad. “An authorized member can create and read a request, with field validation and cross-team access denied” gives an executor and reviewer a usable objective.
Scroll to read all columns
| Mission | Deliverable | Main verification |
|---|---|---|
| Framing | Journeys, criteria and open questions | Decisions needed for the next step are resolved |
| Data and API | Contracts and mutations | Validation, persistence and authorization |
| Interface | Complete user journey | Input, waiting, success, failure and recovery |
| Integration | Journey connected to real data | UI and server describe the same state |
| Deployment | Version accessible on the agreed target | Main journey and production configuration |
Give the pilot a defined role
The pilot follows the mandate and dependencies. It can delegate exploration, implementation and verification to different contributors. It asks for a decision when ambiguity changes the product or exceeds its authority.
Stellary project piloting keeps coordination around cards and their reviews. Tasks can run together when prerequisites and resources allow. A shared repository needs controlled write ownership.
The coordination guide explains how to assign scope. Keep an owner for final integration: three contributions passing independently do not prove that the assembled application works.
Build, then verify independently
The creator returns an identifiable deliverable and the checks it executed. The reviewer opens the result and repeats the acceptance criteria. A screenshot or completion message alone does not establish a working journey.
For the request tracker, check valid creation, invalid input, unauthorized access, signing back in and an interrupted action. Compare displayed state with stored state. Select checks appropriate to the change's actual risks.
An agent supervisor workflow can formalize creation, review and correction. Failed criteria return to the executor with observed discrepancies and reproduction steps. The next stage starts after a sufficient review.
Recover from failures without losing the project
A long mission may encounter an unavailable tool, incompatible dependency or false assumption. The report should identify the last verified result, changed files and useful next action.
Before restarting, inspect whether an action already happened. Recreating an account, migration or deployment can duplicate it. Recovery starts from actual state and remaining work, rather than the agent's latest message alone.
When the same approach fails repeatedly, change strategy or request an independent opinion. More agents do not resolve a missing product decision. Preserve valid work on other branches while the blocker is handled.
Move from development to deployment
Verify the build with the destination configuration. Check service addresses, required variables, permissions, any migrations and the path back to an earlier version. Local success does not establish remote configuration.
Deployment automation depends on granted access and the mandate. A sensitive release may require approval; an explicitly authorized demonstration target can use a different circuit. Describe what actually happened instead of combining every case under the word autonomous.
After deployment, open the served version and repeat the main journey. Check a user error and data preserved after signing back in. Availability means the expected destination serves the correct behavior, rather than a command merely finishing.
Measure full autonomy honestly
Maintain a log containing time, request, role, outcome and any intervention. Separate initial setup, product decisions, approvals, technical restarts and manual code changes.
One outcome may require no manual code but five product decisions. Another may need a direct fix. Both experiences are useful when described accurately. A duration or cost without that context is difficult to compare.
Track time to an accepted journey, observable cost, attempts, discrepancies and interventions. To evaluate coordination, compare one agent and a team on similar scope. Keep acceptance criteria constant and describe the experiment's limits.
Maintain the application after first delivery
The first version creates useful history: decisions, contracts, incidents, checks and deployment instructions. Keep it with the project so the next agent does not rediscover the same constraints.
New features begin with a card and a definition of completion. The prototype-to-product guide covers concerns that grow after the demonstration. A project status report makes accepted work and open decisions visible.
Start with an application small enough to inspect end to end. A useful experiment lets readers see the product, repeat its main journey and understand exactly which work the agents handled.
Review the application with someone outside implementation
Give a user the entry point, an appropriate test account and a task. Observe hesitation, understanding and error handling. A journey that works only with the agent’s explanation is not yet independently usable.
Turn discrepancies into verifiable cards. “The application is confusing” should become a situated observation: after a particular action, the next state is unclear. A corrector can then work without inventing a different product.
This review complements technical checks. Both should apply to the served version being accepted, with the intended data and access. An earlier screenshot cannot establish the behavior of a later correction.
If the conversation is not producing a precise visual choice, building a small tuning tool with AI can unblock the next step. ParamRig supports this kind of exploration before the selected values are integrated into the application.
Preserve a usable handover
At the end, record how to access the application, how to start its normal environment and where configuration is managed. Explain the recovery path and known limitations. Keep credentials in their proper secret system rather than the handover text.
A person maintaining the application should be able to locate the source, identify the deployed version and find the decisions behind important constraints. That handover is part of a complete delivery, even when agents created all the implementation.
If the team wants another agent to continue later, create a bounded next mission with current sources. The existence of a long conversation does not establish which decisions remain authoritative. Clear project documents make future autonomy easier to evaluate and reduce the need to rediscover earlier choices.
Keep evidence of delivery
The status report separates accepted features from work still in review. Keep the version, environment and checked user journey in the same project. Stellary agent orchestration organises contributions; it does not replace acceptance testing of the delivered application.
