AI Agent

An AI agent is an autonomous software system that uses a large language model as its reasoning engine to perceive its environment, make decisions, and take actions toward a defined goal. Unlike a simple chatbot that responds to one prompt at a time, an agent can plan multi-step workflows, invoke external tools, and iterate on its own output until a task is complete.

How It Works

At its core, an AI agent operates in a loop: it receives an observation (user input, tool output, or environment state), reasons about what to do next, selects an action, executes it, and then evaluates the result. This loop continues until the agent determines the task is finished or a termination condition is met.

Modern agents rely on tool use to extend beyond pure text generation. They can call APIs, run code, query databases, read files, and interact with external services. The LLM decides which tool to call and how to interpret the result, effectively bridging natural language understanding with programmatic execution.

Why It Matters

AI agents transform static language models into dynamic problem-solvers. In the context of deployment platforms, an AI agent can serve as a coding assistant, DevOps helper, or customer support operator that autonomously handles multi-step tasks without constant human guidance.

In Practice

When deploying an AI agent (for example, through a platform like Openclaw), you configure which model provider powers its reasoning, which communication channels it listens on (Telegram, Discord, Slack), and what tools it has access to. The deployment infrastructure handles the gateway, authentication, and message routing so the agent can focus on reasoning and acting. Properly scoping an agent's available tools and permissions is critical to both usefulness and safety.