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.
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