Multi-Agent Collaboration: From Writing Code to Project-Level Delivery

1. From One-Shot Generation to Project-Level Execution

Early AI-assisted development was mostly a single-turn loop: ask a question, generate an answer, then edit it by hand. The model produced a function or a document in one pass. People set the task boundary and checked the result. That works for local problems, but it breaks down on engineering work that lasts days or longer and spans many modules. Context is limited, intermediate state is hard to track, and errors accumulate across steps.

Project-level execution changes the job. The system itself has to do four things: split the overall goal into executable subtasks, assign an executor to each subtask, check the results, and merge the parts into something that can run. That requires planning from the model, plus an engineering framework around it for state management and verification. Model capability is only the starting point. Process design decides whether the work can actually be delivered.

2. A Basic Architecture for Multi-Agent Collaboration

A typical collaborative system has three roles.

• Coordinator: reads the overall goal, builds a task graph, assigns work, and rolls up progress.
• Executor: takes a subtask with clear inputs and outputs and does the coding, documentation, or test writing.
• Verifier: runs tests, static checks, or formal checks and returns a pass or fail.

Separating roles keeps responsibilities clear. Executors do not judge whether their own result is correct. Verifiers do not change the implementation. That avoids the problem of grading your own homework and makes it easier to see which stage failed.

The scheduling flow below is simplified. Exception handling and concurrency details are omitted:

function run_project(goal, workers, verifier, max_retries):
    task_graph = planner.decompose(goal)          // build a task graph with dependencies
    while not task_graph.all_done():
        for task in task_graph.ready_tasks():     // tasks whose prerequisites are done
            worker = pick_idle_worker(workers)
            result = worker.execute(task)
            verdict = verifier.check(task, result)
            if verdict.passed:
                task_graph.mark_done(task, result)
            else:
                task.retry_count += 1
                if task.retry_count > max_retries:
                    escalate_to_human(task, verdict.report)
                else:
                    task_graph.requeue(task, feedback=verdict.report)
    return integrate(task_graph.results())

Three points matter. First, only a result that passes verification is written into the task graph. Second, on failure the verification report is attached as feedback for the next attempt, so the model is less likely to repeat the same mistake. Third, retries have a limit. After that, the task goes to a person, so the system does not loop forever on a bad path.

3. Principles for Breaking Down Work

Decomposition quality decides how well the collaboration works. A good subtask usually meets four conditions.

1. Clear inputs and outputs: each subtask states which artifacts it depends on and what it must deliver.
2. Independently verifiable: each subtask has tests or checks, so the verdict does not depend on subjective judgment.
3. Right-sized: a task that is too large makes errors hard to locate and rework expensive; a task that is too small makes scheduling and communication cost more than the work itself.
4. Explicit dependencies: use a directed acyclic graph for ordering, and identify branches that can run in parallel.

Take a small notes app that needs an export feature. One breakdown is:

• T1: define the note data structure and the export field specification (no dependencies)
• T2: implement plain-text export (depends on T1)
• T3: implement HTML export (depends on T1)
• T4: write integration tests for export (depends on T2 and T3)

T2 and T3 do not depend on each other, so two executors can do them at the same time. T4 must wait for both. Total time is then set by the critical path: T1, plus the longer of T2 or T3, plus T4—not the sum of all four tasks.

Interface contracts also need to stay stable. Once T1’s data structure is fixed, treat it as a contract. Later tasks that need to change it must go through the coordinator. Otherwise finished subtasks can silently become invalid.

4. Designing the Verification Loop

Verification is the basis of reliability in a multi-agent system. Collaboration without verification is just faster error generation. Common checks, roughly from lowest cost to highest, include:

• Static checks: syntax, type consistency, and style. Cheapest, and suitable to run after every execution.
• Unit tests: behavior of a single function or module. The most common basis for a pass/fail decision.
• Integration tests: whether interfaces between modules follow the contract. Usually run at integration time.
• Property tests: generate random inputs and check that outputs satisfy invariants. Useful for edge cases.
• Formal verification: prove correctness of critical logic mathematically. Expensive, and suited to safety-critical or financial cases.

Tests should be written before the implementation. The test cases are the specification of the subtask. The executor’s goal is to make the tests pass, not to decide for itself what “done” means. If the same agent writes tests after the implementation, those tests often cover only the logic already written and miss requirements.

The example below states expected behavior and is independent of any particular test framework:

Case: plain-text export preserves heading levels
Input: note title "Weekly report"; body contains a level-1 heading "Progress" and a paragraph
Expected: "Progress" appears on its own line, with a blank line between it and the body
Expected: the output contains no HTML tags or other format residue

The quality of failure feedback matters as much as the check itself. Rewriting “test failed” as “the heading on line 3 is missing a line break; a blank line was expected” gives the executor something specific to fix. A verifier report should include the failure location, the expected result, and the actual result.

5. Integrating Results and Handling Conflicts

When several executors change code at once, the usual problems are file conflicts and inconsistent interfaces. A few practices reduce the risk.

• Ownership by module: one executor owns each module. Others may call it through its interface but must not rewrite its internals.
• Central approval for interface changes: requests to change a public interface go through the coordinator, and dependents are notified after the change.
• Separate commits and rollback points: each subtask’s output has its own version record, so a bad integration can be rolled back to just before a specific subtask.
• Full regression: after integration, run the whole test suite, not only the subtask’s own tests. Subtasks can each pass and still fail when combined.

Integration should also look for hidden assumptions. Two modules may both assume timestamps use the same time zone, or that file names contain no special characters. Unit tests often miss that, and it shows up only when the pieces are combined.

6. Scale Is Not Always Better

Adding executors does not raise throughput in a straight line. Amdahl’s law describes the limit. If the fraction of work that can run in parallel is p, and the number of parallel executors is n, the theoretical speedup is:

S = 1 / ((1 − p) + p / n)

If p = 0.9—ninety percent of the work can run in parallel—then n = 10 gives a speedup of about 5.3, n = 100 about 9.2, and n = 1000 about 9.9. Going from one hundred executors to one thousand barely changes the result. The serial part—integration, global verification, and product decisions—sets the ceiling.

Real systems also pay coordination cost. More executors mean more conflicts over shared state, more duplicated work, and a verification queue that can become the new bottleneck. Size the system to how parallel the work actually is, rather than chasing headcount. For most teams, a handful to a few dozen executors with a clear task graph is more effective than scaling blindly.

7. Model Choice and Cost Control

Most of the cost in a collaborative system comes from model calls. Tasks differ widely in how capable a model they need. Using the strongest model for everything is neither economical nor necessary.

A layered approach fits better. Routine code completion, document cleanup, format conversion, and simple questions can go to a lower-cost model. Call a stronger model only for long-chain planning, open-ended architecture design, or high-risk reasoning.

Task type	Recommended approach	Reason
Format conversion, document polishing	Lower-cost model	Rules are clear and errors are easy to check