AI Agent Architect: Building a Multi-Agent Dialectic System

I am seeking a highly skilled AI Engineer/Architect to help build a proof of concept for "HILCA," a sophisticated, highly dynamic multi-agent system. This is not a standard no-code/low-code automation task; it requires deep experience with custom AI agent orchestration, stateful reasoning, and handling massive context. Core Requirements & Architecture: 1. Dynamic Agent Generation: The system must feature a "Main Controller Agent" that evaluates a user's prompt and dynamically spawns "Sub-Agents" with custom identities, roles, and instructions tailored specifically to that session (e.g., spawning a Physicist, a Philosopher, and a Mystic to debate a specific topic). 2. Dialectic Round-Table Flow: Implement a multi-agent conversational loop where these sub-agents critique, refine, and collaborate across multiple rounds of debate to reach a final consensus. 3. Context & Knowledge Management: Architect a solution for handling very large documents and dynamic knowledge bases introduced by the user per session. You must implement strategies (like advanced RAG pipelines, vector databases, or large-context models with Prompt Caching) to ensure the system never hits token or capacity limits while maintaining full file understanding. 4. State Management: Create a robust, stateful pipeline where the debate history, memory, and agent insights are tracked, documented, and compiled into a final output. Required Technical Skills: • Deep proficiency in Python. • Strong expertise in agent frameworks like LangGraph (highly preferred for cyclic workflows) or AutoGen. • Experience with RAG pipelines and Vector Databases (e.g., Pinecone, Qdrant, Weaviate). • Deep understanding of large-context LLM API handling (e.g., Claude, Gemini) and optimizing token usage/prompt caching. • Experience debugging complex LLM logic loops. If you have built stateful, circular multi-agent architectures before, please apply with a brief summary of a similar system you've engineered.