Learning Timeline
Key Insights
The Benefits of Trajectory in ReAct
Similar to 'Chain of Thought', the ReAct Agent provides a 'trajectory' that allows you to see the AI's reasoning process. This is highly useful for debugging to understand why the AI selects a specific tool or provides a particular answer.
Performance Tip: Async Execution
Use 'async' versions if you need to run multiple tool searches in parallel to speed up Agent response times, especially when processing long data lists.
Prompts
Background Analysis Criteria
Target:
Bio Agent (Gemini 2.5)
Identify instances where a particular person has been at their company for more than 10 years. Use tool calling to get the most up-to-date information and determine if their background is applicable per my criteria.
Step by Step
Building a Bio Agent Module with ReAct
- Define the 'perplexity_search' external function to enable real-time web searches for the AI.
- Define the 'get_url_content' function to allow the AI to extract specific data from discovered links.
- Create a new module named 'Bio Agent' within your development environment.
- Set 'Gemini 2.5' as the primary Large Language Model (LLM) for the module.
- Configure tools within the module so the AI knows it can utilize search functions when up-to-date information is required.
- Build the 'answer' function to process user input and connect it to the LLM.
- Initiate a looping process to allow the Agent to automatically verify criteria (such as employee tenure).
- Monitor the 'trajectory' or ReAct logic logs to observe how the AI makes decisions step-by-step.
