Parallel Code Review with Claude Code Subagents: A Real Use Case for Dynamic Workflows
About This Article
Practical Notes on Claude Dynamic Workflows organized how the parallel subagent mechanism works and which tasks suit it. This article documents my firsthand experience applying that mechanism to code review.
The Problem with a Standard Code Review Request
When asking an AI to review code, the usual approach is to send “please review this code” in a single conversation. The AI then checks multiple aspects in sequence and returns consolidated results.
The issue with this approach is that the more review aspects there are, the longer it takes. If security, performance, and code style could all be checked at the same time rather than one after another, the wait time would be shorter.
There is also a subtler concern: when one conversation handles multiple aspects in order, the conclusion from an earlier aspect can influence how a later aspect is evaluated. With parallel execution, each subagent evaluates independently, which reduces that kind of cross-aspect interference.
The Setup I Used with Dynamic Workflows
I ran three review aspects in parallel.
Subagent 1: Security check Checks input sanitization, authentication and authorization, external dependency versions, and how environment variables are handled.
Subagent 2: Performance check Checks for unnecessary loops, blocking synchronous operations, unnecessary data loading, and whether caching is being used appropriately.
Subagent 3: Code style check Checks naming conventions, type consistency, quality of comments, and adherence to the project’s existing code conventions.
The three subagents ran in parallel, each returning feedback from its own perspective.
Merging the Results
When the parallel execution finished, the results from all three subagents arrived at the orchestrator. The merge step did the following:
- Categorized each piece of feedback by severity (high / medium / low)
- Identified issues flagged by more than one aspect and treated them as higher-priority items
- Organized remediation guidance by aspect and produced the final output
The output was a feedback list organized by aspect, plus a prioritized list of issues that appeared across multiple aspects.
What I Noticed
On processing time: compared to running the three checks in sequence, parallel execution felt faster. That said, Dynamic Workflows consumes more tokens because of the subagents, so a cost comparison requires a separate calculation.
The point where parallel execution felt most effective was the independence between the security and performance aspects. Because neither was influenced by the other’s conclusions, the feedback from each remained distinct.
On the other hand, issues that touched all three aspects simultaneously—for example, a function whose structure had broad implications—required a bit more work to organize during the merge step.
When Parallel Review Is a Good Fit
Parallel review works well when the review aspects are genuinely independent and each aspect’s judgment does not depend on another’s.
Code review, where different perspectives evaluate the same target, seems to be a reasonably good fit for parallelization. Conversely, when the output of one aspect needs to feed into the next as input, running them in sequence is easier to design.
For details on how Dynamic Workflows works, see Practical Notes on Claude Dynamic Workflows.
Summary
- Multiple review aspects can be run in parallel using Dynamic Workflows
- Assigning a subagent to each aspect produces independent evaluations
- The merge step organizes overlapping findings into a prioritized action list
- Parallelization increases token consumption, so the cost-to-speed tradeoff should be confirmed