AI Agent

What is an AI Agent?

An AI agent is a software system in which a large language model plans and carries out multi-step tasks toward a goal, choosing tools such as web search, a browser or APIs, and adjusting based on results. Unlike a single prompt and response, an agent loops: it acts, observes the outcome and decides the next step until the task is complete.

Definitions vary across vendors. Anthropic’s widely cited December 2024 post, “Building effective agents,” distinguishes workflows, “systems where LLMs and tools are orchestrated through predefined code paths,” from agents, where “LLMs dynamically direct their own processes and tool usage.” Marketing material often uses “agent” more loosely for any AI feature, so check what a product actually does.

How it works

Most AI agents combine four parts:

• Model. An LLM that reasons about the goal and decides the next action.
• Tools. Functions the model can call: search, browse, read files, query a database, send an email.
• Memory and context. The conversation, retrieved documents and intermediate results held in the model’s context window.
• Control loop. Code that runs the model, executes tool calls, returns results and enforces limits, such as asking a human before purchases.

Standards are emerging so agents can connect to tools consistently. The Model Context Protocol (MCP), introduced by Anthropic in November 2024, is an open protocol for connecting AI applications to external tools and data. Google’s Agent2Agent (A2A) protocol addresses agent-to-agent communication.

Examples relevant to search: ChatGPT agent (July 2025), deep research modes in ChatGPT and Gemini, agentic features in Google AI Mode, and agent modes in AI browsers.

From SEO to AEO and GEO

In classic SEO, the visitor was a person and the gatekeeper was a ranking algorithm. In AEO and GEO, an AI answer engine became the gatekeeper. With AI agents, the AI can also be the visitor: it reads your pages, compares you with competitors and, in some cases, completes a sign-up or purchase step.

What carries over: crawlable HTML, fast pages, logical site structure and accurate content help agents just as they help search engines. What changed: agents may visit deep pages (docs, pricing, status pages) and act on what they read, so ambiguity or missing facts translate directly into wrong conclusions about your product. Bot policy also gets harder, since some agents act for a specific user rather than crawling.

What to do about it

1. Write for a literal reader. State plan limits, integrations, regions and certifications explicitly. Agents do not infer well from marketing language.
2. Expose structured facts. Use tables, consistent naming and schema markup that matches visible content.
3. Review bot rules. Distinguish training crawlers, search crawlers and user-initiated agents, and decide on each.
4. Consider an MCP server or API. For developer-focused products, giving agents a supported way to query docs or account data can be more reliable than scraping.
5. Test tasks end to end. Ask an agent to “find the pricing and SSO options for [your product]” and check its accuracy.

Myths

• “Agents replace search engines.” Most agents still rely on search indexes to find pages.
• “Every AI feature is an agent.” Many are fixed workflows with an LLM step.
• “Agents always identify themselves.” Some use ordinary browser user agents.

Related terms

• Agentic Search
• Agentic Commerce
• Model Context Protocol (MCP)
• Large Language Model
• AI Crawler
• Retrieval-Augmented Generation
• Guide: How AI engines cite sources

Sources

• Building effective agents, Anthropic (Dec 19, 2024)
• Model Context Protocol, official documentation
• Overview of OpenAI crawlers, OpenAI