AI agent
An AI agent is an AI system that uses a model to plan, choose tools, and take multi-step action toward a goal instead of returning one response.
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What is an AI agent?
An AI agent is a system built around a language model that decides its own next steps. Instead of returning one answer to one prompt, the model reasons about what it needs, calls tools, checks the result, and decides whether to continue or stop, without a person choosing each step in advance. The term covers a range: a single model call with tool access sits at one end, and a system running many rounds of reasoning and action across a long task sits at the other. What makes something an agent is that the model decides what happens next, using the outcome of its last action to choose the one after it.
How is an AI agent different from a context agent?
An AI agent is the general category: any system where a model plans and acts toward a goal. A context agent is Qontext's specific instance of that category, built for one job: creating, editing, and managing files inside a context repository. Every context agent is an AI agent, but most AI agents aren't context agents.
How does an AI agent work?
An AI agent runs a loop rather than a single pass: it takes in a goal and the current state, decides on an action such as calling a tool or reading a file, observes what came back, and decides again whether the goal is met or another step is needed. This loop is what lets an agent handle a task whose exact steps can't be written in advance, because the right next step depends on what the last one returned.
That loop is only as good as what the agent can reach. An agent that plans well but reads fragmented, outdated, or permission-blind information produces a confident answer built on a bad premise. Qontext's context repository gives an agent one structured, current, permission-aware place to work from, so the reasoning loop runs on accurate company knowledge.