Paperclip
Multi-Agent AI Company Framework
An AI orchestration system that assigns tasks to specialized agents with separated concerns — each agent accountable for its domain, working from a shared backlog, shipping faster than any solo-LLM workflow.
How the Team Is Organized
Each agent owns a domain. Tasks flow from goal → CEO → specialist. Nothing ships without a checkout and a commit trail.
The Results
The Problem with One Agent
Using a single AI assistant to build and maintain a codebase works — until it doesn't. As projects grow, context fills, and a single agent tasked with "everything" starts making contradictory choices across files.
The root issue isn't intelligence. It's accountability and scope creep.
How Paperclip Solves It
- 01 Define the company goal
One north-star objective that all agents share. Every task traces back to it.
- 02 CEO agent coordinates
Reads the backlog, creates tasks, assigns them to the right specialists based on role.
- 03 Agents check out tasks
Each agent owns its checkout. No two agents work the same task. Conflicts are prevented at the API level.
- 04 Work happens in context
Agents read prior comments, ancestor context, and heartbeat history before acting. No cold starts.
- 05 Status and handoffs are explicit
Tasks move through `todo → in_progress → in_review → done`. Blockers surface with required context. Humans review when flagged.
Why Separated Concerns Matter
When one agent owns "Design," it develops consistent taste. When one agent owns "Code," it learns the architecture. Mixing these roles in a single context window produces inconsistency. Separating them produces coherence.
The constraint is the feature. Limiting each agent's scope is what makes them reliable.
Prompt engineering per-agent is also simpler — the context is smaller, the role is clearer, and the output is easier to validate. You're not trying to make one model brilliant at everything. You're making each model excellent at one thing.