What Is an AI Agent? How Does It Differ from an LLM?

You can tell GPT-4 'write an email for me' and it returns text. You can tell an AI agent 'send this email, wait for a reply, add a meeting to my calendar' and it actually does it.

Agents are systems where LLMs can access tools (APIs, databases, browsers, file systems) and automatically complete multi-step tasks. The ReAct loop (Reason-Act-Observe) is the core operating logic: think, call a tool, observe the result, think again.

Real-world example: We built an order management agent for an e-commerce company. When a customer says 'where is my order?' the agent calls the order API, connects to the shipping tracking system, calculates alternative delivery options if there's a delay, and gives the customer a personalized answer — all in seconds with zero human intervention.

Challenges of agent architecture: Error handling (what if the agent picks the wrong tool?), Security boundaries (which systems can the agent access?), Cost control (each tool call consumes LLM tokens).

In 2025, agent technology matured. OpenAI's function calling, Anthropic's tool use, and orchestration frameworks like LangGraph make it possible to build real production systems. With the right architecture, an agent can do the repetitive work of a 10-person team in hours.