This tutorial explores how AI models can solve complex, multi-step queries by composing and decomposing function calls - similar to how we break down problems ourselves.
The approach demonstrates how an AI can:
- Decompose a complex task into subtasks
- Execute each part through separate function calls
- Compose results into a final, structured answer
The article connects this to fixed point theory from mathematics, showing how iterative refinement converges to a stable "consensus" result. Includes practical code examples using OpenAI's API.
This tutorial explores how AI models can solve complex, multi-step queries by composing and decomposing function calls - similar to how we break down problems ourselves.
The approach demonstrates how an AI can: - Decompose a complex task into subtasks - Execute each part through separate function calls - Compose results into a final, structured answer
The article connects this to fixed point theory from mathematics, showing how iterative refinement converges to a stable "consensus" result. Includes practical code examples using OpenAI's API.
Related research: https://arxiv.org/abs/2509.11700