AI Agent
An AI agent is an autonomous system in which a large language model (LLM) plans its own steps toward a goal, calls tools, and uses feedback from its environment to iteratively decide what to do next. Unlike a workflow that follows a predefined code path, an agent hands the LLM direct control over how the task gets done.
- An AI agent is a system in which an LLM autonomously plans, uses tools, and iterates in order to reach a goal.
- Anthropic draws a clear line between workflows, where LLMs and tools are orchestrated along predefined code paths, and agents, where the LLM controls its own process and tool use.
- The core building blocks of an agent are planning, memory, and tools; an LLM equipped with these is often called an augmented LLM.
- An agent operates as a feedback loop, taking the ground truth returned by its environment at each step to assess progress and choose its next action.
- This entry focuses on the agent system itself, and is distinct from applied concepts such as the agentic web or agentic search.
What Is an AI Agent
An AI agent is a system in which a large language model (LLM) reasons and makes its own decisions to move toward a goal, without a person spelling out every step. When a user states a goal, the agent plans the steps required, chooses which tools to use—search, code execution, API calls—and repeatedly decides what to do next based on the result of each step. Google describes an agent as an intelligent system that can use reasoning, planning, and memory to act on a user's behalf, emphasizing its ability to look several steps ahead and carry out work across multiple systems.
It is worth noting that this term sits next to, but is not the same as, the agentic web or agentic search. Those cover the applied domains in which an agent browses the web or performs a search, whereas the AI agent discussed here focuses on the structure and operating principles of the agent system itself—the thing that makes those applications possible.
Workflows Versus Agents
In Building Effective Agents, Anthropic splits LLM-based systems into two categories. Both are grouped together as agentic systems, but they behave in distinctly different ways. This distinction serves as a practical yardstick for judging how much autonomy a given task actually requires.
| Aspect | Workflow | Agent |
|---|---|---|
| Control model | LLMs and tools are orchestrated along human-authored code paths | The LLM controls its own process and tool use |
| Path determination | Fixed and predictable | Dynamic, decided at runtime |
| Best suited for | Tasks with clear, repeatable steps | Tasks where the path is hard to predefine and judgment is needed |
| Examples | Prompt chaining, routing, parallelization | An autonomous loop given only a goal and left to work out the solution |
Anthropic's central recommendation is to start with the simplest solution and add complexity only when it is clearly needed. Many tasks call for a well-designed workflow rather than a full agent, because greater autonomy brings greater cost, latency, and unpredictability along with it.
Building Blocks: Planning, Memory, and Tools
The foundation of an agent is the augmented LLM—an LLM enhanced with retrieval, tools, and memory. Rather than simply generating text, such a model can write its own search queries, select the right tools, and decide what to remember. Wang et al.'s widely cited survey, A Survey on Large Language Model based Autonomous Agents (arXiv:2308.11432, 2023), proposes a unified framework spanning a large body of prior work and organizes autonomous agents around profile, memory, planning, and action modules.
Planning
Planning breaks a complex goal into executable sub-steps, sequences them, and revises the plan as intermediate results come in. It is the core of an agent's ability to work through problems that cannot be solved correctly in a single shot.
Memory
Memory stores conversation history and the results of past steps so they can be used for multi-turn interaction and reasoning. Google likewise explains that memory retains history, enabling multi-turn conversation and supporting the agent's reasoning.
Tools
Tools bring in capabilities the LLM lacks on its own, such as web search, code execution, and calls to external APIs. Because a tool's output becomes the real-world information that drives the next action, designing tools with clear documentation and testing has an outsized effect on agent performance.
How It Works: The Feedback Loop
The essence of an agent lies not in a single response but in an iterative loop. As Anthropic describes it, an agent plans independently, uses tools based on ground truth from the environment at each step, assesses its own progress, and pauses to gather human feedback at intermediate checkpoints when needed. For this autonomous behavior to work reliably, it must rest on clear evaluation criteria and on trust in the agent's decision-making.
Google Vertex AI describes the same skeleton. An orchestration layer manages a reasoning loop that combines model output with tools, handling multi-step workflows and deciding when to call a tool in order to produce more accurate responses.
Implications for SEO and GEO
The rise of AI agents changes the assumptions behind search and content strategy. Instead of people clicking through and reading pages themselves, agents increasingly gather, compare, and synthesize information on their behalf. From an operator's standpoint, a few considerations stand out.
- Because the output a tool returns—search results, API responses—becomes the basis for the agent's decisions, structured content that machines can reliably parse and clear factual statements grow in importance.
- Since an agent moves by evaluating the actual results from its environment rather than isolated snippets, pages that offer authoritative sources, consistent data, and verifiable information are more likely to be cited and used.
- This connects directly to GEO, which optimizes content to be cited in generative search; AI agents are the underlying technology that powers those applications.