SideProjectAI
← All Playbooks
🐝

The Indie Founder Agent Swarm Dev Playbook

Run parallel AI dev agents that ship features while you sleep

For technical solo founders who want to multiply their output by running multiple AI coding agents simultaneously instead of sequentially. This playbook sets up an orchestrated agent environment where each agent has persistent memory, isolated sandboxes, and structured task assignments — so you wake up to merged code, not a pile of conflicting diffs. It's the closest thing to a dev team without payroll.

Goal

Run multiple parallel AI coding agents that ship features without blocking each other

Who this is for

Technical indie founders and solopreneurs who want to scale output beyond a single AI coding session

When to use

When you have more than 3 parallel feature branches or bug fixes to work through at once

When NOT to use

If you are still scoping your MVP and don't yet have a stable codebase or task list to parallelize

$40–$120/mo~120 min setup

How to set it up

1

Set up your agent task queue and assign work

Create your feature and bug-fix tasks in Tokanban. Define each task precisely with acceptance criteria so agents can pick them up without ambiguity. Assign 3–5 tasks to start.

2

Spin up isolated sandboxes for each agent

Launch one isolated sandbox per agent task using the local sandbox tool. Ensure each sandbox has its own API keys and repo branch so no two agents write to the same environment.

3

Enable persistent memory across all agents

Install Memoir via MCP and configure it for each agent session. Seed each agent's memory with your codebase conventions, architectural decisions, and any prior context relevant to their task.

4

Launch and monitor parallel agent sessions

Use the tmux-based orchestration tool to start all agents simultaneously. Monitor each pane, redirect any blocked agent, and let TaskTrace log all activity so every agent stays contextually aware.

5

Review and merge agent PRs in logical chapters

As agents submit pull requests, use Stage to organise them into reviewable chapters. Review, approve, and merge each one sequentially so your main branch stays clean and intentional.

1

Manage multiple AI agents with tmux-based orchestration

Visit →

Manages multiple AI agents in parallel tmux sessions so you can monitor, pause, and redirect individual agents without losing context on the others.

Free
2

Run AI coding agents in isolated sandboxes with secure API key handling

Visit →

Gives each agent a fully isolated local environment with secure API key handling so agents can't stomp on each other's work or leak credentials.

Paid
3

Give AI coding tools persistent memory between sessions

Visit →

Persists each agent's understanding of your codebase and decisions across sessions so they don't re-ask the same questions or repeat solved problems.

Freemium
4

AI agents manage tasks natively, no UI friction or bypasses needed.

Visit →

Assigns tasks to agents natively without UI friction — agents pick up, update, and close tasks as they work, giving you a real-time view of what's shipping.

Freemium
5

AI code review that organizes pull requests into logical chapters for clarity

Visit →

Organises the flood of pull requests from parallel agents into readable chapters so you can review and merge confidently without losing track of what changed.

Freemium
6

Local activity log that gives LLMs full context of your work

Visit →

Logs all local activity so every agent always has full context of what other agents and you have already done, preventing duplicated or contradictory work.

Freemium

Expected outcome

A multi-agent dev environment where 3–5 agents work parallel tasks with memory, sandboxing, and clear task assignments — shipping multiple features per session

Was this playbook useful?

This playbook is a curated starting point, not a definitive recommendation. Pricing and features change — always verify on each tool's official website. Tools marked "affiliate link" may earn this site a commission at no extra cost to you.