Mastering Agents: Build And Evaluate A Deep Research Agent with o3 and 4o

The text discusses the development of a financial research agent using the LangChain framework. The agent is designed to tackle complex research questions by breaking them down into smaller, manageable steps, and then re-planning based on what it has learned. The agent uses a combination of natural language processing (NLP) and web search capabilities to gather information and make informed decisions. The project aims to create a research roadmap that guides the agent through its research journey, ensuring it stays focused and efficient. The evaluation process involves setting up a Galileo evaluation callback to track and record performance, running experiments with test questions, and analyzing results to identify areas for improvement. The project's findings suggest that the agent performs well in terms of context adherence and speed, but may struggle with tool selection quality and providing proper sources for older data.

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