AI Agents: Looping vs Planning

How to Implement AI Agents: Looping vs Planning

1. Introduction to Looping vs Planning Agents

   • In this tutorial, we will explore the concepts of looping versus planning agents in the context of AI technology.
   • The focus will be on understanding how to fine-tune language models and create efficient AI systems that can predict and execute tasks effectively.

2. Understanding the Data Structure

   • The data structure for an agent should be a plan that can be executed deterministically.
   • This plan should be able to take in requests and produce the correct plan for execution.

3. Predicting Tools Needed for a Request

   • Given a request, the first step is to predict all the tools needed to accomplish the task.
   • This may involve multiple steps or tools, and it could require a recommendation system to suggest additional tools based on the initial set.

4. Generating an Execution Plan

   • Once the tools are identified, create an execution plan that outlines how these tools will be used to fulfill the request.
   • This plan serves as a blueprint for the system and can be iteratively updated based on conversations with the model.

5. Iterative Plan Refinement

   • Continuously refine the execution plan based on feedback and conversations with the model.
   • The goal is to reach the correct plan that efficiently executes the task at hand.

6. Hydrating the Plan with Examples

   • Retrieve examples of successfully running plans to further enhance the execution plan.
   • These examples provide insights into how similar tasks were completed successfully in the past.

7. Implementing Individual Steps

   • Break down the execution plan into individual steps or nodes with connections.
   • Implement each step to create a comprehensive plan that can be executed seamlessly.

8. Using Few-Shot Examples

   • Utilize few-shot examples to enhance the execution plan further.
   • By providing examples of transitions between tools or steps, the model gains a better understanding of the task at hand.

9. Creating a Probabilistic Plan

   • While building the plan is probabilistic, the execution should be deterministic.
   • Focus on creating a solid plan that can be executed reliably to achieve the desired outcome.

10. Fine-Tuning Models

    • The ultimate goal is to produce artifacts that can be used to fine-tune models efficiently.
    • By iterating on the input-output process, aim to predict outputs accurately in a single shot, streamlining the system.

By following these steps, you can implement looping and planning agents effectively in AI systems, improving task execution and model performance.