AI Agent

Fundamental Differences from Traditional Chatbots

Traditional AI assistants operated in a "one question, one answer" format, where a user asks a single question and receives a single response. AI agents differ from this model: given a goal, they independently construct the intermediate steps required to achieve it, performing API calls, file operations, web searches, and more as needed, before returning a final deliverable. Their defining characteristic is that humans do not need to provide step-by-step instructions.

How They Work

AI agents generally operate on the following loop:

1. Observe — Grasp the current situation (user instructions, tool outputs, error messages, etc.)
2. Think — Reason about what action to take next
3. Act — Perform concrete operations such as calling external tools, executing code, or editing files
4. Iterate — Observe the results and repeat until the goal is reached

Underpinning this loop are the reasoning capabilities of LLMs (Large Language Models) and tool-connectivity mechanisms such as MCP (Model Context Protocol).

Why They Are Attracting Attention in 2026

Coding agents capable of autonomously handling software development tasks—such as Claude Code, Devin, and OpenAI Codex—have reached the stage of practical use. Furthermore, in the realm of enterprise workflow automation, "multi-agent" architectures, in which multiple agents work in coordination, are becoming increasingly widespread. Google's A2A (Agent-to-Agent) protocol is being developed as a standard specification enabling agents from different vendors to communicate with one another.

However, granting agents excessive permissions carries the risk of unintended operations. In production environments, it is essential to incorporate sandboxed execution and approval workflows for critical operations.