LangChain and AI Agents — When LLMs Get Tools

ChatGPT answers questions. An AI agent takes action. The difference? An agent has access to tools — it can search databases, call APIs, execute code, and decide on its own which tool to use and when.

From Chatbots to Agents¶

An agent receives a task and actively works to complete it. Needs data? It writes SQL. Needs current information? It calls an API. The LLM functions as the agent’s brain — the ReAct pattern: Reasoning + Acting.

LangChain — Anatomy of the Framework¶

• Models: Wrapper over LLMs (OpenAI, Anthropic, Hugging Face…)
• Prompts: Template system with variables and few-shot examples
• Chains: Operation sequencing (query → retrieve → generate)
• Agents: Autonomous decision-making with tool access
• Memory: Conversational memory (buffer, summary, vector store)

Challenges and Gotchas¶

Hallucinated tool calls. Agents occasionally call nonexistent tools. Robust error handling is a must-have.

Infinite loops. A max iterations limit is mandatory.

Cost control. A complex task can mean 10–20 API calls.

Agents Are the Future of AI Applications¶

Start simple — one agent, two tools, a clear use case. Then scale gradually.