Explainer

What is an AI agent?

An AI agent is software that pursues a goal by acting — not just answering. Give it an objective and it plans steps, calls tools, reads results and keeps going until the job is done or a human gate stops it.

Agents act; chatbots answer

A chatbot’s output is text you act on. An agent’s output is the acted-upon result: the report filed, the ticket created, the inbox triaged. The model is the same species; the difference is a runtime that lets it use tools, hold memory and be held to account.

What a real agent needs

Four things separate production agents from demos: tools it may use, memory that persists between runs, boundaries — budgets, permissions, approval gates — and a record of what it did. Remove any one and you have either a toy or a liability.

Where they earn their keep

Agents shine on work that is frequent, structured enough to describe, and judgment-heavy enough that plain automation fails: triage, enrichment, monitoring, first-draft anything. They do the 80% that is pattern; you keep the 20% that is taste.

How to tell a real one from a demo

Ask four questions. Which tools can it use, and who decided that? What does it remember between runs? What is it forbidden from doing without a human? And can you read what it did afterwards? A system with good answers to all four is an agent you can give work to. A system with a compelling video and no answer to the last two is a demonstration, and the gap between the two is where most disappointment with agents actually comes from.

Common questions

Are AI agents safe for real work?

With boundaries, yes: scoped tools, spend caps, human approval on consequential actions, and receipts you can audit. Safety is architecture, not hope.

Do I need to code to run one?

Not in AGNT — download it, connect a model, and give an agent a job in plain language or install a working one from the marketplace.

What is the difference between an agent and a workflow?

A workflow is a path you designed in advance; an agent decides what to do next. Most production systems use both — fixed steps for the predictable parts, agent steps where judgment is genuinely required.

Give AI a job. Get the proof.

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