What Is an AI Agent? The Complete Technical Reference

If you ask an LLM to “cancel my subscription,” it will output a polite template telling you how to click the cancel button yourself. If you ask an AI agent to do the same, it will authenticate to your billing system, execute the API call to cancel the subscription, verify the status code, and email you a confirmation.

The difference between a Large Language Model (LLM) and an AI agent comes down to a single word: action.

An LLM is a stateless text predictor. An AI agent is an autonomous software system that uses an LLM as its reasoning engine to perceive its environment, make decisions, execute tools, and iteratively achieve a complex goal.

This reference guide breaks down the technical architecture of an AI agent and how these systems are built for production.

The Core Architecture of an AI Agent

While the term “agent” is heavily overloaded, a true AI agent consists of four fundamental components: the reasoning engine, tools (actuators), memory, and a planning framework.

1. The Reasoning Engine (LLM)

The LLM is the brain of the agent. Instead of simply generating human-readable text, the agent uses the LLM to process input, analyze state, and output structured decisions—typically in JSON format. The model evaluates what it knows, what it needs to know, and what tool it must call next to bridge the gap.

2. Tools (Actuators)

Tools are how the agent interacts with the outside world. Without tools, an agent is trapped inside its context window. A tool can be anything from a simple Python calculator to a complex REST API call. Frameworks like the Model Context Protocol (MCP) standardize how agents discover and execute these tools. When the LLM decides to take an action, it outputs the exact function signature and arguments needed to invoke the tool.

3. Memory (State Management)

Because LLMs are inherently stateless, agents require external memory to maintain continuity across long-running tasks.

• Short-Term Memory: The immediate context of the current task. This is typically managed by passing the execution history (a list of prior tool calls and their results) back into the prompt.
• Long-Term Memory: Persistent storage (like a vector database) where the agent can recall past interactions, user preferences, or historical knowledge that doesn’t fit into the immediate context window.

4. Planning and Orchestration

Agents do not simply execute linearly. They must break down a massive goal (e.g., “Research our competitors and build a pricing table”) into manageable sub-tasks. They use cognitive architectures like ReAct (Reason + Act) or graph-based state machines (like LangGraph) to iterate. The agent plans a step, executes a tool, evaluates the result, and then either proceeds to the next step or handles the error and recalculates its plan.

Agentic Workflows vs. True Autonomous Agents

Not every system that calls an API is a fully autonomous agent. It is helpful to distinguish between deterministic workflows and autonomous agents.

Agentic Workflows: The developer hardcodes the graph of execution. The LLM is only used at specific nodes to make routing decisions or extract data. The system has tight guardrails and a predictable execution path.

Autonomous Agents: The developer only provides the tools and the goal. The LLM dynamically constructs the entire execution graph on the fly, deciding how many steps to take, which tools to use, and when the task is complete.

For production enterprise systems, developers almost exclusively rely on heavily constrained agentic workflows (like those built with LangGraph) because true autonomous agents are unpredictable and impossible to secure reliably.

Frequently Asked Questions

What is the difference between an LLM and an AI agent?

An LLM generates text based on a prompt. An AI agent uses an LLM as a reasoning engine to dynamically execute tools, interact with external systems, and accomplish multi-step goals.

Do AI agents write their own code?

While some coding agents (like Claude Code or Cursor) generate and execute code within a sandboxed environment to solve specific tasks, typical enterprise agents execute predefined tools (APIs, database queries) rather than compiling custom software on the fly.

What is the best framework for building AI agents?

As of 2026, LangGraph is the dominant framework for building production agents due to its robust state management and check-pointing. AutoGen and CrewAI remain popular for multi-agent prototyping, while the Model Context Protocol (MCP) has become the standard for tool integration.

Are AI agents secure?

Out of the box, no. Because they execute arbitrary actions based on natural language, they are highly vulnerable to prompt injection and tool poisoning. Production agents require strict least-privilege tool access and human-in-the-loop (HITL) approvals for state-mutating actions.