AI Agent

An AI agent is an artificial intelligence system that goes beyond simple question-and-answer interactions to autonomously plan, reason, and execute multi-step tasks. While a standard LLM responds to a single prompt, an AI agent can break a complex goal into subtasks, use external tools, browse the web, interact with APIs, and iterate on its own outputs until the objective is met.

How AI Agents Work

The Agent Loop

AI agents operate through an iterative cycle of reasoning and action.

1. Observe - The agent receives a task or perceives its current state
2. Think - The agent reasons about what to do next, often using chain-of-thought prompting
3. Act - The agent takes an action, such as searching the web, running code, or calling an API
4. Evaluate - The agent reviews the result of its action
5. Iterate - If the task is not complete, the agent returns to step 2 with updated information

Core Capabilities

Capability	Description	Example
Planning	Breaking complex tasks into subtasks	Decomposing “research competitors” into search, analyze, compare steps
Tool Use	Interacting with external software and APIs	Browsing the web, executing code, querying databases
Memory	Retaining information across steps	Remembering findings from earlier research steps
Reasoning	Making logical decisions about next actions	Choosing which search query to run based on prior results
Self-Correction	Identifying and fixing errors	Re-running a failed search with a better query

Types of AI Agents

ReAct Agents

ReAct (Reasoning + Acting) agents alternate between reasoning steps and action steps. The model explicitly writes out its thought process before deciding what action to take, making its decision-making transparent.

Tool-Using Agents

Tool-using agents are equipped with a set of external tools, such as web search, code execution, file reading, and calculator functions. The agent decides which tool to use at each step based on the task requirements.

Multi-Agent Systems

Multi-agent systems use multiple specialized agents that collaborate on a task. Each agent handles a different aspect of the work, and a coordinator agent manages the workflow.

Architecture	Structure	Strengths	Challenges
Single Agent	One model handles everything	Simple, coherent	Limited by one model’s capabilities
ReAct Agent	Reason-then-act loop	Transparent reasoning	Slower due to explicit reasoning
Tool-Using Agent	Model plus external tools	Extensible, capable	Tool integration complexity
Multi-Agent	Multiple specialized agents	Division of labor, scalability	Coordination overhead

AI Agents in the Real World

Web Browsing Agents

Browsing agents can navigate websites, click links, fill out forms, and extract information from web pages. They effectively automate the kind of research that a human would do manually in a browser.

Coding Agents

Coding agents can write, test, debug, and deploy code. They can read existing codebases, understand requirements, propose changes, and execute tests to verify their work.

Research Agents

Research agents can conduct multi-step investigations, searching across multiple sources, synthesizing findings, and producing comprehensive reports. These agents are particularly relevant to AI search, as they essentially perform deep research on behalf of the user.

Customer Service Agents

AI agents are increasingly deployed in customer service, where they can access customer records, troubleshoot issues, process requests, and escalate to human agents when necessary.

AI Agents and Search

How Agents Change Search Behavior

Traditional search requires the user to formulate queries, evaluate results, refine searches, and synthesize information. AI agents automate this entire process.

Traditional Search	Agent-Powered Search
User writes a query	Agent plans multiple queries
User evaluates 10 blue links	Agent reads and analyzes pages
User refines and re-searches	Agent iterates automatically
User synthesizes information	Agent produces a comprehensive answer
Single search session	Multi-step research process

Agentic Search Platforms

Several platforms have introduced agent-powered search features.

• Perplexity Pro Search - Conducts multi-step research with follow-up queries
• Google’s AI Mode - Uses agent-like capabilities for complex queries
• ChatGPT Deep Research - Browses the web extensively to compile detailed reports
• Claude’s Computer Use - Can interact with software and web browsers directly

Implications for Content Visibility

How Agents Discover Content

AI agents discover content differently from traditional crawlers or even standard AI search.

• Multi-query discovery - An agent may find your content through a series of related searches, not just one query
• Deep page analysis - Agents can read and process entire pages, not just snippets
• Cross-referencing - Agents compare information across multiple sources to verify accuracy
• Follow-up exploration - Agents may follow internal links to explore your site more deeply

Content Characteristics Favored by Agents

• Comprehensive coverage - Agents seeking thorough answers favor in-depth content
• Factual accuracy - Agents cross-reference sources, so inaccurate content gets filtered out
• Clear structure - Well-organized content is easier for agents to parse and extract from
• Internal linking - Strong internal linking helps agents discover related content on your site
• Up-to-date information - Agents prioritize current, recently updated sources

Building Trust with AI Agents

AI agents evaluate source credibility as part of their reasoning process. Content from authoritative, well-established sources with strong EEAT signals is more likely to be trusted and cited by agents.

Trust Signals That Matter

• Demonstrated expertise in the subject area
• Consistent publishing history on the topic
• Proper citation of sources within your own content
• Transparent authorship and editorial standards
• Technical reliability (fast loading, no broken links)

Why It Matters for AEO

AI agents represent the next evolution of how users interact with AI search. Instead of answering single questions, agents conduct extended research sessions, visiting multiple pages, comparing sources, and synthesizing comprehensive answers. This changes the dynamics of content visibility in important ways.

For AEO practitioners, the rise of AI agents means that content must be optimized not just for single-query retrieval but for multi-step discovery and cross-referencing. Content that is accurate, comprehensive, well-structured, and well-linked is more likely to be found, trusted, and cited by AI agents during their research process. As agent-powered search becomes more prevalent, the quality and depth of your content will matter even more than it does today.

Genrank helps you track how AI systems, including emerging agentic search platforms, discover and cite your content, giving you the insights needed to optimize your AEO strategy for the agent-driven future of search.