Design patterns for building AI-native organizations with agent.ceo
A cyborgenic organization is an AI-native operating model where human leaders and AI agents work together as a unified team. Instead of bolting AI onto existing workflows, a cyborgenic organization is designed from the ground up with agents as first-class participants — they have roles, responsibilities, communication channels, and accountability, just like human team members. The agent.ceo platform provides the infrastructure to build, manage, and scale these organizations.
Reusable design patterns for common cyborgenic workflows:
| Pattern | Description |
|---|---|
| Multi-Agent Task Delegation | How to decompose work and delegate across a team of specialized agents |
| Knowledge-Base-Driven Agents | Using shared knowledge bases to give agents organizational context |
| Enterprise Discovery & Onboarding | Patterns for discovering what agents an enterprise needs and onboarding them |
| Agent Meeting Protocol | Structured communication between agents for decision-making and sync |
| Observable Loop (Observer / Observed) | A standing critic (observer) judges builders' solutions against better alternatives on evidence and drives swaps, looping to convergence — without writing the code itself |
| Agentic Recursion (Delegate-and-Return) | An agent calls an agent of its choice, which calls another, … until a stop condition; the result returns up the stack. Pausable/stoppable, editable stop-condition + choice-set |
| Ralph Loop (Fresh-Context Task Drain) | The primitive build engine: fresh context every iteration, one task per pass, state externalized to repo + progress file + git; drain a task list to empty. Composes as the Observed builder inside an Observable Loop |
| AutoResearch Loop (Propose · Run · Measure · Keep-or-Revert) | The primitive search engine (Karpathy AutoResearch / self-improving): an LLM edits code, runs a real experiment, scores one objective metric, keeps on improve else reverts; swarm in parallel operands, observer promotes winners to scale |
Ready-to-use CLAUDE.md configuration templates for common agent roles:
| Template | Role |
|---|---|
| Backend Python | Python backend engineer agent |
| Fullstack TypeScript | Full-stack TypeScript/React agent |
| DevOps | Infrastructure and deployment agent |
End-to-end examples of cyborgenic organizations:
| Example | Description |
|---|---|
| Simple Org | Minimal 3-agent organization for a SaaS startup |
- Explore the patterns — Read through the patterns to understand common approaches
- Pick a template — Start with a template that matches your agent's role
- Configure your org — Use the examples as a starting point for your organization config
- Deploy on agent.ceo — Sign up at agent.ceo and deploy your agents
- agent.ceo Platform: https://agent.ceo
- Developer Docs: https://agent.ceo/developers/docs
- Python SDK: agent-ceo-sdk
We welcome contributions! Whether it is a new pattern, an improved template, or a full example:
- Fork this repository
- Create a feature branch:
git checkout -b feat/my-pattern - Add your content following the existing structure and conventions
- Submit a pull request with a clear description
Please keep patterns practical and include code examples where applicable.
This project is licensed under the MIT License — see the LICENSE file for details.
Built by GenBrain AI — powering cyborgenic organizations with agent.ceo