How to prototype an LLM-driven agentic reasoner that replicates GPT-4o1’s 'System 2' thinking

The video discusses the design and engineering of an LLM-driven agentic reasoner that replicates GPT-4o1’s 'System 2' thinking. This is achieved by decomposing abstract reasoning problems into smaller sub-problems, then utilizing a multi-agent system architecture to collaboratively address these challenges. The video explains the architecture of this system and demonstrates its functionality using an AI Voice Assistant prototype.

To build the agentic reasoner, the following algorithms, techniques, and approaches were used: LLM algorithms (GPT-4.0), advanced prompting (meta-prompting), autonomous agents, and multi-agent system design. The LiveKit framework was utilized for the demo. The video includes a working demo that illustrates how abstract reasoning and plan decomposition can be implemented with these technologies. Additionally, my custom multi-agent system prototype, which supports parallel decision-making and task resolution, is described and used to enable distributed and cooperative problem-solving. The prototype, developed in Python, is integrated into a fully functional AI Conversational Assistant using the LiveKit framework. This setup allows the AI agent to determine if and when the conversation should be escalated to a human operator—one of the key challenges in AI assistant design. The video demonstrates one approach to addressing this challenge using advanced agentic reasoning.

[therealsergio]

You get at one of the the foundational problems: should agentic systems be orchestrated, or should they self organize. I believe a good mind (and a good kitchen) has both mechanisms in parallel. There are emergent behaviors, and there are orchestrated behaviors. As long a behavior is itself data that the agentic system can reflect and update on, you can kind of enjoy the best of both approaches.