How to break down multi-Agent tasks: Parallelization decisions, conflict resolution, and acceptance checklist
Adding more Agents does not automatically produce faster results. If branches wait on one another, modify the same file simultaneously, or lack conflict-resolution rules during aggregation, parallel execution will turn into duplicated effort. A more reliable starting point is to map dependencies first, then divide work by deliverable, allowing each branch to complete, fail, and rerun independently.
Step 1: Map dependencies before assigning roles
First, rewrite the overall objective as a set of verifiable deliverables, then specify the input, output, prerequisites, and final consumer for each one. Role names only indicate who performs the work; they do not show whether tasks are independent of one another.
| Relationship | How to assess it | How to handle it |
|---|---|---|
| Sequential | B must read A’s result before it can begin | Complete A before starting B |
| Parallel | A and B only read the same frozen source material, and neither writes to the other’s target | Run them simultaneously |
| Parallel, then aggregate | Multiple branches produce outputs independently and require a unified decision only at the end | Run them in parallel and have a single aggregator finalize the result |
For example, “research Agent → writing Agent → review Agent” is still a sequential chain, while verifying multiple independent sources can be done in parallel. When dividing the work, specify at least the input, output, and completion criteria, and make shared source material a read-only snapshot.
Step 2: Run tasks in parallel only when all four conditions are met
- Fixed inputs: Once a branch starts, it does not depend on continuously changing upstream content.
- Isolated writes: Each branch has a unique ID, file, or record and does not compete for the same write target.
- Consistent output structure: All branches use the same fields and format, making automated validation and aggregation easier.
- Isolated failures: A timeout or failure in one branch does not contaminate results already completed by other branches.
Logical independence is not enough. Account rate limits, file locks, and external API capacity also constrain concurrency. Set a concurrency limit, timeouts, and a limited number of retries instead of starting every branch at once.
Step 3: Make the aggregator genuinely resolve conflicts
Aggregation is not copying and pasting. Each branch can be required to return a conclusion, evidence URLs, collection time, and unresolved uncertainties. When contradictions arise, compare sources, timestamps, and statistical definitions. If the evidence is insufficient, preserve the disagreement rather than manufacturing a single answer for the sake of consistency.
- Check whether every planned branch has returned a result
- Identify duplicate records while retaining their sources
- Flag conflicting evidence, missing items, and outdated information
- Reserve side-effecting actions such as publishing, deletion, and payment for a single executor
Final writes that have side effects should occur only after aggregation and human confirmation. This prevents upstream reruns from causing duplicate publications, repeated deletions, or multiple external effects.
Step 4: Run a failure drill first
Start with two or three low-risk, read-only branches and deliberately make one of them time out. Then check whether the other branches can still finish, whether the failed branch can be retried independently, whether the aggregated result clearly shows the missing item, and whether rerunning creates duplicate records. Gradually increase concurrency only after the test passes.
Pre-launch acceptance checklist
- Each branch has a unique identifier and completion marker
- Shared inputs are frozen, and write targets are isolated from one another
- Output fields are consistent, and missing fields can be detected
- Failures do not overwrite completed results, and retries are idempotent
- Conflicting information retains its evidence and timestamps rather than being forcibly merged
- The final deliverable can be traced back to its branches and evidence
- Actions with side effects are executed only once
Which tasks are unsuitable for parallel execution
Tasks that continuously modify the same document, depend on the same session state, share an account that does not support concurrent use, or require each subsequent strategy to be adjusted based on the previous result should generally be run sequentially. SmaugBrain can support parallel branches, but the workflow designer must still define input boundaries, write permissions, and acceptance rules. The value of parallelism comes from independence, not from the number of tasks.