· Engineering
Scale AI Without Breaking Your Repos: How FlytBase Runs Parallel Coding Agents Without Chaos
Imagine opening your laptop and spinning up not one AI coding assistant, but ten — one fixing UI bugs, another writing API integrations, a third reviewing pull requests, two more debugging flaky tests in parallel. Now imagine all of them touching the same repository at the same time. That’s where things get messy.
During a recent AI in Action session, Mahesh, a senior software engineer at FlytBase, unpacked how the team solves one of the biggest challenges in AI-native software development: orchestrating multiple coding agents without turning the workflow into complete chaos.
Because while AI can dramatically accelerate code generation, scaling agents across real production repositories introduces a new category of problems — merge conflicts, runaway worktrees, overloaded machines, fragmented reviews, and developers drowning in terminal windows. The FlytBase approach isn’t about replacing engineers. It’s about building a workflow where humans and agents collaborate at speed without sacrificing reliability.
The Hidden Problem With “More Agents”
Most developers start simple: open a chat-based coding assistant, ask it to build a feature, review a bug, or refactor a component. It works surprisingly well. Then comes the obvious next thought — what if multiple agents could work on different tasks simultaneously?
The common approach is git worktrees: each agent gets its own isolated branch and workspace so changes don’t collide immediately. On paper, elegant. In practice, exhausting — multiple terminals, duplicate environments, parallel app instances, disconnected review flows, ballooning disk usage, vanishing RAM. Mahesh described this as the point where “AI acceleration” can accidentally make teams slower instead of faster.
Why FlytBase Chose a Multi-Workspace Workflow
Rather than building custom orchestration from scratch, the team combined git worktrees with a multi-workspace IDE — Conductor. What made it valuable wasn’t some magical AI capability, but that it wrapped complex worktree management inside workflows developers already understand: worktree creation, repository setup scripts, isolated terminals, scoped file views, diff inspection, and pull request generation, all handled automatically.
“AI workflows only scale when operational complexity disappears.”
The Three-Step Workflow FlytBase Uses
1. Strategy first, execution second. The team starts with higher-level planning using powerful reasoning models — often Claude Code for brainstorming, architecture discussions, and planning complex implementation flows — converting ideas into explicit, step-by-step plans before handing off execution.
2. Fast executors handle the heavy lifting. Once the plan is ready, FlytBase runs Cursor Composer through the CLI directly inside Conductor workspaces, letting multiple agents execute tasks in parallel while staying isolated from each other — one building a feature, another updating tests, another handling documentation.
3. Humans stay in the review loop. FlytBase does not blindly merge AI-generated code. Every workspace feeds into structured review pipelines — automated review agents, PR templates, CI checks, human verification, diff inspection. AI should reduce mechanical effort, not eliminate engineering judgment.
The Real Unlock: Breaking Work Into Independent Chunks
Parallel agents only work well when problems are decomposed correctly. Large features need to be split into independent, merge-safe tasks — otherwise multiple agents start editing overlapping files and conflicts explode. That’s not an AI problem. It’s an engineering systems problem.
Solving Resource and Environment Chaos
Running several worktrees locally used to consume massive system resources — frontend instances fighting over ports, containers duplicating unnecessarily, RAM spiraling out of control. The team introduced dynamic port allocation, avoided unnecessary Docker port bindings, shared setup scripts, and leaner isolated environments. The result: several concurrent agents while staying under 2GB of RAM.
If You Want to Try This Yourself
- Use git worktrees for isolation
- Automate workspace setup scripts
- Enforce strong PR and CI checks
- Keep execution scoped to well-defined tasks
- Add multi-workspace tooling only when coordination becomes painful
And perhaps most importantly: treat planning as seriously as execution. The best results come when agents receive structured, machine-readable implementation plans instead of broad instructions — that single shift dramatically improves reliability.
The Bigger Shift
Software development itself is becoming orchestration work. Developers are no longer just writing code — they’re coordinating systems of reasoning models, execution agents, review workflows, infrastructure layers, and operational guardrails. The future probably won’t belong to teams with the “smartest” individual agents. It’ll belong to teams that build the cleanest collaboration systems around them.