What are AI agents?
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AI agents are software systems that pursue goals and complete tasks on behalf of users. They plan, act, observe, and adapt across sequences of steps, without requiring human approval at each one. That autonomy distinguishes an agent from a chatbot or an AI assistant.
For publishers and SEOs, agents are now active across search and content discovery. Research agents break complex queries into sub-tasks and synthesise findings across multiple sources. Monitoring agents scan the web continuously, delivering summaries when conditions match a user’s criteria. Personal agents like Gemini Spark compile digests and execute tasks across applications on a user’s behalf. Each type affects how content is retrieved, consumed, and attributed differently from standard search.
What is an AI agent?
An AI agent is software that takes a high-level goal, decomposes it into steps, selects and uses tools to execute each step, evaluates the results, and adapts until the goal is achieved. The large language model (LLM) at the core provides reasoning; the agent framework provides the tools, memory, and execution environment that let reasoning translate into action.1
Key characteristics:
- Reasoning and planning: the agent determines what steps are needed before acting, not just what to say in response
- Tool use: the agent can call external systems (search, APIs, databases, applications) as part of completing a task
- Memory: context is maintained across steps within a task, and often across sessions
- Autonomy: the agent makes decisions about how to proceed without step-by-step human direction
How do AI agents differ from AI assistants and chatbots?
Google Cloud draws this distinction explicitly:1
| AI agent | AI assistant | Chatbot | |
|---|---|---|---|
| Purpose | Autonomously completes tasks | Assists users on request | Responds to triggers |
| Autonomy | High: acts independently | Medium: user makes decisions | Low: follows pre-defined rules |
| Complexity | Multi-step, adaptive | Simple to moderate tasks | Basic interactions |
| Example | Gemini Spark, Information Agents | Gemini chat, ChatGPT | FAQ bots |
The editorial implication: Google AI Mode is an AI assistant. It responds to user queries and can hold a conversation, but the user drives each step. Google Information Agents are agents. The user sets a criterion; the agent acts continuously and independently to monitor for it. Treating the two as equivalent produces confused optimisation advice.
What are the main types of AI agents?
Two dimensions matter for understanding agents in the context of search and content.
By interaction pattern
Interactive agents (also called surface agents) are user-query triggered. The user initiates a task; the agent responds. Perplexity Deep Research and ChatGPT’s research mode are interactive agents: the user submits a question and the agent runs a sequence of sub-searches to synthesise an answer. The user started the process.1
Background agents (autonomous background processes) are event-driven and always on. The user sets a criterion; the agent monitors continuously and acts when conditions are met, without the user initiating each cycle. Google Information Agents and Gemini Spark’s monitoring features fall into this category. The user may never initiate a search for information the agent delivers.1
By coordination
Single agents operate independently to complete a task. Multi-agent systems involve multiple agents coordinating on complex work, each potentially running a different model. When Gemini Spark delegates a research sub-task to a specialised retrieval agent, an agent-to-agent protocol manages the handoff. Publishers do not implement this directly, but it shapes how multi-step retrieval chains resolve. The agent protocol stack is covered in the WebMCP article.
How do AI agents work?
Four components underpin how agents operate:1
Model: the LLM that reasons, plans, and generates outputs. Gemini Spark uses Gemini 3.5 Flash (agentic variant). Different agents may use different models suited to their task.
Tools: external capabilities the agent can call: web search, file access, calendar APIs, third-party applications. Tools extend what the agent can do beyond generating text.
Memory: agents maintain context across the steps of a task (short-term) and across sessions (long-term). This is what allows a monitoring agent to remember what conditions to watch for, or a personal agent to recall a user’s preferences across weeks.
Persona and instructions: each agent has a defined role, scope, and communication style. These instructions determine how it behaves and what it is permitted to do.
What does the rise of AI agents mean for search and content visibility?
Four agent types are now operating across search and content discovery, each with different implications.
Research agents (Perplexity Deep Research, ChatGPT research mode, Google’s research features) handle complex, user-initiated queries by breaking them into sub-tasks and synthesising findings across sources. Content that consolidates multiple relevant sub-answers reduces how many sources the agent needs to visit. Traditional ranking still applies: agents draw from top-ranked pages for each sub-query. See Agentic SEO.
Background monitoring agents (Google Information Agents) scan continuously for user-defined criteria and deliver synthesised updates. Content may be consumed without a click, without a Search Console impression, and without a GA4 session. Freshness matters differently here: a monitoring agent returns to sources repeatedly, routing past content that has become stale. See Agentic SEO and Google AI Mode.
Personal agents (Gemini Spark) run on cloud infrastructure, connect to a user’s applications and tools via MCP, and can monitor topics and compile digests from web content on a schedule the user sets. Brand visibility may occur entirely outside the search interface, with no measurable signal in current analytics tools.
Action agents use protocols like WebMCP to call functions on websites rather than just read them. An agent shopping on behalf of a user calls a searchProducts or checkAvailability function rather than navigating a page. For most informational sites this is not yet relevant; for e-commerce, travel, and booking sites it represents the next layer of infrastructure. See WebMCP.
The common thread: standard analytics records human sessions. Agent evaluations, monitoring passes, and digest compilations produce no signal in GA4, Search Console, or server logs. Measurement across this layer is an open problem.
What to do now
For most publishers, the foundational work is the same across all agent types: accurate, well-structured content with clear entity signals. Content that earns citations in standard AI search earns inclusion in agent synthesis for the same reasons. No separate agent-specific implementation is needed for informational and editorial sites.
For e-commerce, travel, booking, and SaaS products, the action layer matters. Structured data, checkout API compatibility, and MCP server exposure become relevant as agents capable of taking transactions move from Beta to broad availability.
Frequently asked questions
Is Gemini (the chat product) an AI agent?
No. Gemini chat is an AI assistant: reactive, session-based, responding to user prompts. Gemini Spark is an AI agent: it runs continuously on cloud infrastructure, takes actions proactively, and operates when the user is offline.
Are AI Overviews powered by AI agents?
AI Overviews use RAG (Retrieval-Augmented Generation) retrieval rather than agentic multi-step planning. They run a retrieval pass and synthesise an answer in one query cycle. Related in architecture but distinct: an agentic research system runs multiple retrieval passes, cross-references sources, and resolves contradictions before synthesising.
Do agents crawl sites the way Googlebot does?
Retrieval agents use search index results as their starting point: they query against whatever is already indexed and ranked, rather than crawling independently. Action agents (WebMCP-enabled) interact at the browser or API layer at the moment a user’s agent takes action, not during indexing. Standard crawlability and indexability determine what retrieval agents can reach.