AI agent
An AI system that pursues a goal by taking actions — calling tools, reading data, making decisions — rather than only generating text. Where a chatbot answers, an agent does: it plans steps, executes them against real systems, observes results and adjusts.
What separates an agent from a chatbot
A chatbot returns text and stops; you are the one who acts on it. An agent is judged on the state of the world after it runs — the inbox triaged, the ticket filed, the report written. The underlying model is often identical. What differs is the runtime around it: permission to call tools, memory that survives the session, and a record of what it did with both.
The four things a production agent needs
Tools it is allowed to use, and only those. Memory, so the tenth run is better than the first. Boundaries — spend caps, scoped permissions, approval gates on anything consequential. And a receipt, so a human can check the work afterwards. Remove tools and you have a chatbot. Remove memory and it relearns your business every morning. Remove boundaries or receipts and you have something nobody should let near production.
Where agents actually pay for themselves
The sweet spot is work that is frequent, describable, and just judgment-heavy enough that rule-based automation gives up: triage, enrichment, monitoring, first drafts of anything. An agent absorbs the eighty percent that is pattern and hands you the twenty percent that needs taste. Work that is rare, or where being wrong once is catastrophic, stays manual or sits behind a gate.
In AGNT
In AGNT, agents hold real jobs with assigned tools, persistent memory and receipts for every action.
AI agent — common questions
What is the difference between an AI agent and an LLM?
The LLM is the reasoning engine; the agent is the engine plus everything that lets it act — tool access, memory, permissions and a loop that keeps going until the goal is met. An LLM answers a question once. An agent works a task until it is done or blocked.
Are AI agents safe to run against real systems?
With boundaries, yes. Scope the tools narrowly, cap the spend, put an approval gate in front of anything that sends, pays or deletes, and keep receipts you can audit. Safety here is an architecture decision, not a matter of trusting the model.
Do I need to write code to run an AI agent?
Not in AGNT. Download it, connect a model provider, and describe the job in plain language — or install a working agent from the marketplace and edit it on the canvas.