Agent

When you ask ChatGPT or Claude a question, you get a single response. But real-world tasks require multiple steps: research, decide, and execute. An AI agent is an execution paradigm that, given only a goal, autonomously creates a plan, uses the necessary tools, and completes tasks while verifying results along the way.

How It Differs from Chatbots

The biggest difference from traditional chatbots is autonomy. A chatbot simply answers one question at a time, but an agent calls the LLM repeatedly to run a loop of thinking and acting toward achieving a goal. For example, given the instruction "research Competitor A's latest press release, summarize the key points into three items, and post them to Slack," an agent autonomously executes a sequence of steps: web search, information extraction, summary creation, and Slack API call.

The Plan-Execute-Observe-Revise Cycle

At the core of an agent is the plan-execute-observe-revise cycle. In the framework called ReAct (Reasoning + Acting), the LLM reasons about what to do next, calls a tool, observes the result, and revises the plan as needed. This mechanism allows for a degree of flexibility in handling unexpected situations.

Tools Define Capabilities

Agents use a wide variety of tools. Web search, file operations, database queries, API calls, code execution -- operations that were impossible for an LLM alone are executed through Function Calling and MCP. Since the number and quality of tools directly determine an agent's capabilities, building out the tool ecosystem has become an industry-wide trend.

Practical Examples and Challenges

Examples of agents entering practical use include software development assistance (GitHub Copilot Workspace, Devin), automated customer support, and data analysis automation. Anthropic's "Computer Use" feature, released in 2025, allows Claude to operate a mouse and keyboard while viewing a screen, greatly expanding the possibilities for agents.

However, challenges remain. In complex tasks, a wrong decision midway affects all subsequent steps, making the balance between ensuring reliability and human oversight (human-in-the-loop) critical. Currently, the collaborative model -- where humans verify at key points while AI executes -- is more common than full autonomy.