Build a complete application with AI agents, from idea to deployment
Plan a complete application with AI agents: requirements, data, UI, testing, corrections and deployment. Track the human input needed for real autonomy.
8 min read
Running three AI agents in parallel can reduce waiting time. It can also produce three incompatible implementations, two competing migrations, and an integration that takes longer than the original work.
Parallel work is useful when missions can advance independently, have explicit boundaries, and return comparable evidence.
When should you use multiple AI coding agents?
Use several agents when the work truly separates: analyzing different areas, writing independent tests, preparing documentation while a feature is developed, or implementing modules that share an already stable contract.
Keep one agent when decisions are sequential: choose the data model before the API, settle the API before the interface, or diagnose a failure whose cause is still unknown. Parallelizing dependent questions does not remove their logical order.
OpenAI’s multi-agent orchestration guide distinguishes centralized management, where one manager retains control, from handoffs that transfer control to a specialist. For coding work, that choice must be completed with file ownership and an integration order.
Separate
Find a boundary that does not depend on an open decision.
Assign
Name the files, contract, and outcome each agent owns.
Reunite
Decide who integrates, in which order, and with which tests.
A 6-step method for coordinating agents in parallel
- Write the shared outcome. One sentence describes what a user can do after the contributions are combined.
- Map dependencies. Mark decisions that must come first: schema, API contract, shared component, or file format.
- Split by ownership, not volume. “Do half” is vague. “You own the validator and its tests” creates a verifiable boundary.
- Give everyone the same starting point. Use the same Git revision, project rules, criteria, and contracts.
- Isolate workspaces. A branch or worktree per contribution prevents two agents from writing to the same index or interpreting each other’s unfinished changes.
- Integrate in order of increasing risk. Contracts and tests first, implementations next, and the interface last when it depends on backend behavior.
Git documents worktrees as multiple working trees attached to one repository, each with its own HEAD and index. They isolate edits; they do not resolve design conflicts. That remains a coordination problem.
A good split reduces shared surfaces
The number of tasks is not the right signal. Look at how many decisions and files they share.
Likely collision
Two agents change the same form
One adds fields, the other changes validation, and both rewrite the same component.
Cleaner boundary
Contract, backend, and interface are sequenced
The contract is settled first. Backend work and tests can then advance before visual integration.
Useful parallelism
Documentation and exploratory testing
Both can advance from the same expected behavior without modifying the same files.
If two missions need to change the same contract, do not launch them together. Settle the contract once, then redistribute work from that new shared source.
Every mission needs a contribution contract
An agent instruction should describe more than what to build. It should state what the agent owns and how it hands work back.
Expected outcome: an authorized user downloads the filtered report currently shown on screen.
Ownership: export service, backend route, and related tests. The filter component and permission model remain unchanged.
Shared contract: from, to, and status parameters, UTF-8 encoding, and column order approved before work starts.
Evidence: service test, strict route validation, trial with empty and active filters, and a sample output file.
Return: changed files, decisions made, untested limits, and possible conflicts for the integrator.
This format protects the agent too: it knows what it may change, what belongs to someone else, and how its contribution will be evaluated.
Integration is a separate mission
Contributions that compile independently can still fail after they are combined. The integrator must verify the system after each addition, not merely resolve Git conflict markers.
The integration sequence is:
- reread the expected outcome and shared contracts;
- integrate the contribution that defines the contract;
- run its tests;
- integrate consumers one by one;
- replay the complete user journey;
- document deviations and integration decisions.
A successful merge means Git combined the files. It does not mean the behaviors are compatible. Use our guide to verifying an AI agent’s work to keep those states separate.
Keep humans and agents on the same card
In Stellary, each mission can keep its outcome, owner, sources, dependencies, and validation on the board. Stable decisions remain in the knowledge base, while agents receive useful context without turning a conversation into the permanent source of truth.
The goal is not to display more agents. It is to see which work is moving, which boundary may change, and which contribution is waiting for integration.
A prompt to prepare the split
Split a change between multiple agents
Analyze the request and repository without modifying files.
First state the shared user outcome. Then identify decisions that must be settled before any parallel work: data, APIs, shared components, permissions, and formats.
Propose independent missions. For each one, name the owner, allowed files or modules, elements that must remain unchanged, dependencies, tests, and handoff format.
Flag every pair of missions that would touch the same surface or depend on an open decision.
Finish with the integration order and the end-to-end validation journey. Do not execute any mission before the split is approved.
Parallelize waiting, not confusion
Multiple agents are effective when they remove idle time without multiplying versions of the truth. A shared baseline, ownership boundaries, and explicit integration matter more than the number of agents running.
Start with two genuinely independent missions. Measure integration time, rework, and collisions. If coordination costs more than the waiting time it saves, reduce parallelism and improve the split.
Coordinate the project with a Stellary pilot
Project piloting starts from a human-enabled mandate: objective, success criteria, pilot, authorized team and milestones requiring human decisions. The pilot delegates contributions and follows dependencies between cards. Contributors can be agents or people.
Coordination answers three questions: what can start, who has the context and access, and what permits acceptance? It complements file ownership rather than replacing it.
Reserve shared resources
A shared write resource stays reserved for the contribution using it. The piloting contract serializes a shared repository by default, as well as work with unknown write scope. Lease expiry alone does not free a resource while its execution is still active.
Parallel work remains useful for independent tasks. Read-only exploration can prepare alternatives while an executor implements a change. The pilot still needs an integration order and a check of the combined outcome.
Move from a reported result to acceptance
A finished mission submits its result for review. Acceptance requires criteria and independent inspection of the deliverable. An executor's completion message alone does not close the card.
Repeated failures should change the next action: strategy, assignment or a decision request. The contract requires a different strategy or executor after two failed corrections with the same configuration. An explicit question preserves the blocker without repeatedly calling the model while nothing changes.
Separate project coordination from a workflow circuit
The pilot selects the next work under the project mandate. A workflow defines a reusable circuit such as creation, verification, correction and approval. A coordinated contribution can follow that circuit using compatible tools.
The supervisor workflow guide explains roles. The business adoption guide helps choose an initial scope, while the application guide provides an observable delivery definition.
Prepare usable coordination
Link briefs and sources to cards, check the team and execution capabilities, then define the review. Engines do not support every role under identical restrictions: availability as an executor does not establish compatibility as a pilot.
Start with contributions whose dependencies are explicit. Keep decisions, questions and outcomes with the project. A project status report can then distinguish accepted deliveries from open decisions.
Frequently asked questions
How many AI agents should work in parallel?
There is no universal optimum. The useful ceiling depends on independent missions, shared code surfaces, and the team’s ability to review and integrate the output.
Is a Git branch enough to prevent collisions?
It isolates edits, not decisions. Two branches can implement incompatible contracts. You also need clear ownership and a shared source for structural choices.
Who should integrate agent work?
A person or agent explicitly responsible for the whole. The mission includes merge order, tests after each contribution, and validation of the complete journey.
Turn the task split into a working setup
The supervised workflow defines handoffs between researcher, implementer and reviewer. AGENTS.md and CLAUDE.md instructions set repository rules. The agent orchestration page describes Stellary’s setup; for an external client, start with Claude Code or Codex.
These guides describe a method and documented configuration. They do not prove that a team will save time: record conflicts, rework and the integrated result to evaluate your own trial.
