Scaling Development with Parallel AI Agents

Source: Dev.to

Overview

I’ve been experimenting with a workflow that multiplies developer productivity by running multiple AI agents in parallel, each working on its own feature branch. The result is several features being developed simultaneously while I supervise.

Process

1. Define Tasks

Scan your codebase for TODOs, planned features, or backlog items. Transform each into a well‑structured prompt that gives the agent enough context to work autonomously.

Example prompt

## Task: Implement user authentication
- Add login/logout endpoints to /api/auth
- Use JWT tokens with 24h expiration
- Follow existing patterns in /api/users
- Write tests in /tests/auth/

Clear scope and references to existing patterns help agents follow established conventions.

2. Create Separate Worktrees

Instead of constantly switching branches, use git worktree to create independent working directories for each feature.

# Create worktrees for each feature
git worktree add ../feature-auth feature/auth
git worktree add ../feature-dashboard feature/dashboard
git worktree add ../feature-export feature/export

Now you have three separate directories, each on its own branch.

3. Launch Agents

Start a Claude Code (or similar) agent in each worktree.

# Terminal 1
cd ../feature-auth && claude

# Terminal 2
cd ../feature-dashboard && claude

# Terminal 3
cd ../feature-export && claude

Each agent works independently without conflicts—no branch switching, stashing, or merge conflicts while working.

4. Supervise and Guide

While the agents work, monitor their progress and provide guidance at decision points (e.g., business logic or architectural choices). The mindset shift is that you’re reviewing proposals and steering direction rather than writing code line‑by‑line.

Review Bottleneck

When 5–10 PRs are generated in an hour, manual review becomes the chokepoint. The limiting factor shifts from code generation to code review.

Automated Code Review

Claude Code can perform reviews on its own output. Run a review pass before creating the PR:

“Review this branch for bugs, security issues, and adherence to project conventions. Be critical.”

This catches obvious issues before they reach human review.

Atlassian’s Rovo Dev Agent

For teams on Bitbucket, Rovo can automate parts of the review process. It’s still early, but the direction is promising.

MCP Integration

For Bitbucket users, I’ve built an MCP Server that enables Claude to interact directly with PRs—viewing diffs, adding comments, and managing the review workflow through natural language.

Best Practices

Start Small

Don’t launch 10 agents on day one. Begin with 2–3 parallel features and build your supervision skills.

Define Clear Boundaries

Each agent should work on isolated features. Overlapping scope leads to merge conflicts and wasted effort.

Use Consistent Prompts

Create a template for task prompts. Consistency helps agents produce predictable output.

Review Before Merge, Not After

Catch issues in the PR stage. Once merged, fixing problems is more expensive.

Outlook

The future isn’t about writing more code—it’s about orchestrating agents that do it well. This workflow has fundamentally changed how I think about development capacity: a single developer can now realistically manage multiple feature streams simultaneously.

Shifting Skills

• Prompt engineering for clear task specification
• Architecture for defining clean boundaries
• Review efficiency for maintaining quality at scale
• Orchestration for managing parallel workstreams

Walkthrough

I recorded a full walkthrough of this workflow:

Watch the full walkthrough on Loom (link omitted)

Contact

Experimenting with similar workflows? I’d love to hear what’s working for you—get in touch.

Originally published on javieraguilar.ai

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