Fundamentals
Understand the core concepts behind CCCC's multi-agent orchestration approach. Learn about evidence-driven collaboration, peer challenge, and production-minded development.
What is CCCC?
CCCC (Collaborative Code Co-Creation) is a multi-agent orchestration framework designed for production-minded software development. Unlike single-agent AI tools or chatbot interfaces, CCCC coordinates multiple AI agents that work as peers, challenging each other's assumptions and collaboratively pushing projects forward.
Core Philosophy
CCCC is built on three fundamental principles:
1. Evidence-First Collaboration
Every decision, change, and strategic shift is documented and justified. CCCC maintains:
- POR.md (Plan of Record): Strategic board tracking overall project direction
- SUBPOR.md: Per-task tracking with detailed evidence trails
This approach ensures:
- Full transparency of AI decision-making
- Auditability of all changes
- Context preservation across sessions
2. Multi-Agent Peer Challenge
Single AI agents suffer from:
- Direction drift: Losing focus on original goals
- Narrow vision: Missing alternative approaches
- Mental decay: Context degradation over long sessions
CCCC addresses these through peer collaboration:
- Multiple agents propose solutions independently
- Agents challenge each other's assumptions
- Consensus emerges from constructive debate
- Quality improves through mutual correction
3. Production-Minded Orchestration
CCCC is not a chatbot or IDE plugin—it's an autonomous orchestrator that:
- Makes small, reversible commits
- Operates continuously in the background
- Focuses on shippable, production-ready code
- Minimizes human interruption while maintaining control
How Multi-Agent Collaboration Works
The Peer Model
CCCC typically deploys:
- Two primary agents: Co-equal peers (default: Claude Code and Codex CLI)
- Optional auxiliary agent: For complex tasks requiring additional perspectives (e.g., Gemini CLI)
Each agent:
- Proposes solutions based on the current task
- Challenges peer proposals with counterarguments
- Refines approaches based on peer feedback
- Documents decisions in POR.md/SUBPOR.md
Evidence-Driven Workflow
Task Assignment → Peer Proposals → Challenge Phase →
Consensus Building → Implementation → Evidence Logging → Commit
Every step is:
- Documented: Why decisions were made
- Reversible: Small commits enable easy rollback
- Auditable: Full transparency into AI reasoning
Key Benefits
Addresses Single-Agent Limitations
| Challenge | Single Agent | CCCC Multi-Agent |
|---|---|---|
| Context loss | Frequent restart needed | Smart context management |
| Direction drift | No correction mechanism | Peer challenge prevents drift |
| Low verification | Human must catch all errors | Agents verify each other |
| Opaque decisions | Black box reasoning | Full evidence trail |
Remote Work Enablement
CCCC integrates with:
- Telegram: Monitor and control from mobile
- Slack: Team notifications and oversight
- Discord: Community collaboration
Work from anywhere—orchestrate agents from your phone during commute, at coffee shops, or while traveling.
Cost Efficiency
- Use existing subscriptions: Claude, ChatGPT, Gemini
- No API token costs: Leverage Pro/Plus/Advanced subscriptions you already pay for
- Flexible allocation: Scale agent usage based on task complexity
Design for Deep Work
CCCC is built for:
- Long-term projects: Context management enables weeks-long work
- Complex tasks: Multi-agent coordination handles sophisticated challenges
- Precision control: Fine-tune resource allocation and agent behavior
- Universal application: Not just code—research, content, strategy, analysis
Repository-Native Tracking
Unlike external tools, CCCC is repo-native:
POR.md (Plan of Record)
Strategic board containing:
- Current project goals
- Major milestones
- Strategic decisions and rationale
- High-level progress
SUBPOR.md (Sub-Plan of Record)
Per-task tracking with:
- Detailed implementation steps
- Agent debates and consensus
- Evidence for each decision
- Fine-grained progress
Both files live in your repository, ensuring:
- Version control of decision history
- Seamless team collaboration
- No external dependencies
- Audit trail preservation
Continuous, Trustworthy Progress
CCCC prioritizes:
- Small, reversible changes: Each commit is focused and reviewable
- Autonomous operation: Minimal human interruption
- Transparent reasoning: Full visibility into AI thinking
- Collaborative correction: Agents catch each other's mistakes
Not a Chatbot UI
CCCC differs from ChatGPT/Claude interfaces:
- No conversational back-and-forth: Agents work autonomously
- No context window limits: Repo-native tracking preserves context
- No manual prompting: Orchestrator manages agent interactions
- Production focus: Built for shippable code, not experiments
Universal Orchestration
While initially designed for code development, CCCC's framework applies to:
- Content creation: Multi-agent writing and editing
- Research: Collaborative analysis with peer validation
- Business strategy: Debate-driven decision making
- Project planning: Architectures refined through challenge
- Documentation: High-quality docs with iterative refinement
Wherever peer challenge and evidence-driven workflows add value, CCCC thrives.
Next Steps
Now that you understand CCCC's fundamentals:
- Learn detailed Installation procedures
- Explore Configuration options
- Master Usage patterns and workflows