Decision Flowchart for Agents to get that extra accuracy

If you are struggling with your AI agent's reliability, you may not be the only one out there.

Beyond Model Selection, Data Quality, & Eval Testing, This article outlines a structured decision flowchart for AI Agent developers to improve reliabiity for their AI agent use-case. It guides agent developers through selecting and refining approaches based on reliability needs, ultimately leading to a more effective implementation of Agentic application. Needless to say, data & evals are supercritical for the decision flow to work.

1. Start with Selecting Right LLM or LLMs for the specific task

• Identify the best-performing model available for your specific task.
• If reasoning capabilities are required, ensure the model supports this feature.

2. Improve Fewshot examples, make them prompt relevant if required

• If more reliability is needed, proceed to prompt tuning.
• Implement prompt tuning by adding few-shot examples to enhance agent performance. Make few shot examples context/prompt relevant.

3. Assess further reliability needs

Option 1: Break Down the Problem into smaller tasks

• Decompose the problem into smaller software steps.
• Integrate LLMs as nodes within a larger software workflow (e.g., Steps, Directed Acyclic Graph (DAG), Tree structure).

Option 2: Collect and Fine-Tune

• Gather a sufficient number of trajectories relevant to the specific use case.
• Fine-tune the model specifically for all combination of trajectories.

4. Combine Fine-tuning and Software based workflow

If further reliability is still desired, combine both approaches:

• Implement the software workflow while also fine-tuning the model with collected examples.
• This hybrid approach maximizes the effectiveness and reliability of the LLM in your application.

By following this flowchart, Agent developers can systematically enhance the reliability and performance of their models, ensuring they meet the specific needs of their projects.