# Building Your First AI Agent with LangChain (Part 2: The Practical Implementation)

> Source: <https://pub.towardsai.net/building-your-first-ai-agent-with-langchain-part-2-the-practical-implementation-a390b06d625f?source=rss----98111c9905da---4>
> Published: 2026-07-28 12:31:03+00:00

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# Building Your First AI Agent with LangChain (Part 2: The Practical Implementation)

## In **Part 1**, we covered the mental models behind single-agent systems, dissecting the **ReAct (Reasoning + Acting)** framework and learning how tool docstrings guide tool selection.

Now, in **Part 2**, we move from theoretical concepts to hands-on execution. We will build a production-ready **AI Job Hunter Agent** using Python, LangChain, Google Gemini, Tavily, and Streamlit.

## 1. The Architectural Pivot: Decoupling Retrieval

A common pitfall with pure ReAct agents is giving the model open-ended, live web search access directly inside its execution loop. This often causes two major issues:

**Token Inflation & High Latency:** Repeated searching drains context limits and slows down response times.**Non-Deterministic Loops:** The LLM can get stuck tweaking query parameters without reaching a conclusion.