The CAMELOT Framework
Context-Aware Multi-Expert Layered Orchestration Technology for AI agent systems
CAMELOT is my framework for orchestrating multiple AI agents into coherent systems. The name stands for Context-Aware Multi-Expert Layered Orchestration Technology.
Core Principles
Context Awareness
Agents need to understand not just their task, but their position in the larger system. A summarization agent behaves differently when it’s feeding into a decision engine vs. producing human-readable output.
Multi-Expert Architecture
No single model or prompt can handle every situation. CAMELOT uses specialized agents for different cognitive tasks, routing between them based on task requirements.
Layered Orchestration
Like Meadows’ leverage points, there are layers of control:
- Task Layer — What specific action is being performed
- Context Layer — What state and history inform the task
- Strategy Layer — What approach governs multiple tasks
- Goal Layer — What outcome the system is trying to achieve
Technology Agnosticism
The framework isn’t tied to any specific model or provider. The principles apply whether you’re using Claude, GPT, or local models.
Why This Matters
The current approach to AI agents is often either:
- Single-agent systems that try to do everything (brittle, confused)
- Multi-agent systems with no coherent orchestration (chaotic, redundant)
CAMELOT provides the missing middle: structure that enables specialization while maintaining coherence.
Relation to System-of-One
System-of-One is my personal implementation of CAMELOT principles—a single-person operation augmented by well-orchestrated AI agents.
Current Status
The framework is mature in concept but still evolving in implementation. Core patterns are stable; specific tooling continues to develop.