Debating AI Agents: How LLMs Work Together to Solve Complex Problems

Key Concepts:

• Agentic AI
• Multiple LLMs
• Non-deterministic workflow
• Debating agents
• Model reconsideration
• OpenAI
• XAI

1. Introduction to Agentic AI and Multiple LLMs

• The video focuses on Agentic AI and its techniques, particularly using multiple Large Language Models (LLMs) working together.
• Agentic AI often involves a non-deterministic workflow, where the process's end point isn't predetermined.
• A third aspect of Agentic AI, not covered in detail in this video, is the use of tools to interact with the real world (e.g., scheduling appointments, sending emails, calling APIs).

2. Debating Agents for Solving Math Problems: Example 1

• The presenter introduces the concept of using debating agents to improve the accuracy of LLMs in solving math problems.
• Problem: A treasure chest contains 175 diamonds, 35 less rubies than diamonds, and twice as many emeralds as rubies.
• Process:
  • Agent 1 (LLM 1) is given the problem and provides a solution (e.g., 255).
  • Agent 2 (LLM 2) is given the same problem and provides a different solution (e.g., 595).
  • Agent 1 is re-asked the question, but this time it's provided with Agent 2's rationale and result, prompting it to reconsider its answer.
  • Agent 2 is re-asked the question, but this time it's provided with Agent 1's rationale and result, prompting it to reconsider its answer.
  • The process continues until both agents converge on the same answer.
• Result: In the example, both agents eventually agreed on the correct answer (595) after reconsideration.

3. Debating Agents for Solving Math Problems: Example 2

• Problem: How many integers between 1 and 1,000 are divisible by three, five, or seven, but not three and five?
• Setup: Two debating agents are used: OpenAI and XAI.
• Round 1:
  • OpenAI provides an answer of 486.
  • XAI provides an answer of 477.
• Round 2:
  • OpenAI is given XAI's feedback and changes its answer to 477.
  • XAI is given OpenAI's feedback and changes its answer to 486.
• Round 3:
  • OpenAI sticks with 477.
  • XAI changes its answer to 477.
• Result: The agents converge on the correct answer (477) after multiple rounds of debate and reconsideration.
• The number of rounds is non-deterministic; the process continues until the agents agree, with a possible limit to prevent infinite loops.

4. Key Arguments and Perspectives

• Using multiple LLMs in a debating format can significantly improve the accuracy of solutions to complex problems.
• The non-deterministic nature of the process allows the system to resolve itself through iterative feedback and reconsideration.

5. Agentic AI Principles Reinforced

• Multiple LLMs: Using multiple agents to improve results.
• Non-deterministic Workflow: The number of rounds required for convergence is not predetermined.
• Tools (Not Covered): Agentic AI often uses tools to interact with the external world.

6. Conclusion

• Agentic AI, particularly the use of debating agents, is a promising technique for improving the performance of LLMs in complex tasks.
• The combination of multiple LLMs, non-deterministic workflows, and the potential use of external tools enables more robust and accurate problem-solving.