What are AI Agents, Really? › Agents Are Not Magic

<TLDR> An **agent** is a **non-deterministic state machine** made of functions that call LLMs or tools, guided by memory and control logic to reach a goal. Each function is a **state**, and transitions depend on outputs from LLMs or tools. Agents: - Combine decision-making, tool use, and memory - Use components like LLM calls, tool calls, memory, and branching logic - Finish when a defined condition is met, which could be binary, subjective, or iterative **Tools** are real-world actions agents can trigger, such as APIs. These are often discovered dynamically using the **Model Context Protocol (MCP)**. **Memory** can be short-term, long-term, scratchpad, or shared. It is crucial for retaining context across states or agents. **Agent swarms** are multiple agents that coordinate on subgoals, similar to how departments function within a company. **The key challenge** is orchestration. This means managing non-determinism, evolving APIs, and error handling. > *An agent is a structured, memory-aware workflow that uses LLMs and tools to act toward a goal.* </TLDR> The term "agent" shows up everywhere: agentic workflows, autonomous agents, agent swarms. But if you're not exactly sure what an agent is, you're not alone. This space moves quickly, and terminology can make it harder to grasp the core ideas. This article provides a clear mental model of agents: what they are, how they work, and why they matter. We will explore their mechanics, design principles, and practical guidance for building agentic systems. > **Tip:** Before continuing, read [this article on Tools and the Model Context Protocol (MCP)](/writing/introduction-tools-mcp-model-context-protocol). It is foundational for understanding how agents interact with their environment. Online commentary often describes agents as AI teammates capable of reasoning like humans, writing code from scratch, or replacing entire departments. The truth is more practical. Agents are structured workflows. They are programs composed of logic, memory, tools, and language model calls. These are organized in a way that lets them operate autonomously, at least partially, toward a specific goal. So what makes them special? They combine decision-making, tool use, and stateful reasoning. This is something that LLMs alone cannot reliably accomplish.