AI Agent

A chatbot answers; an agent acts. Given a goal, "find and shortlist three CRM options under our budget", an agent decomposes the task, calls tools (web search, browser, code, APIs), evaluates intermediate results, and loops until done. Reasoning models supply the planning ability; tool-use frameworks and protocols like MCP supply the hands.

Deep research features, computer-use assistants, and autonomous shopping or booking flows are all agents, and their share of commercial research is growing fast.

Agents visit websites as automated readers: fetching pages, parsing structure, extracting pricing and specs, comparing vendors, then reporting conclusions to a human who may never see your site. Design, animations, and persuasion aimed at human attention are invisible to them; machine-readable clarity, clean HTML, structured data, explicit facts, accessible pricing, is everything.

This shows up in analytics as rising bot traffic from AI user agents, traffic that converts indirectly, by determining whether the agent's report recommends you.

Audit your site as an agent experiences it: can key facts be extracted from raw HTML without JavaScript gymnastics? Is pricing stated, not gated? Do comparison-relevant specs exist in parseable form? Add llms.txt and schema markup, and keep AI crawlers permitted. Agent-readability is rapidly becoming a conversion factor.

The agent ecosystem also creates new interfaces for your own data: Geonimo exposes its visibility analytics through an MCP server, letting your team's AI assistants query brand-mention data directly inside their own agentic workflows.