AI Agents
An AI agent is software that can perceive context, decide on actions, and carry them out — sending messages, calling tools, taking real steps — rather than just answering a single prompt with text.
A chatbot answers what you type. An agent goes further: given a goal or a trigger, it can read relevant context, decide what to do next, call tools or APIs, and take multiple steps toward an outcome — sometimes with no human in the loop for each step. That's the practical distinction: a bot replies; an agent acts.
Acting has consequences a reply doesn't, which is why the interesting engineering problem with agents isn't "can it decide what to do" — modern models are decent at that — but "how do we bound what it's allowed to do, and prove what it actually did." An agent with unrestricted tool access and no audit trail is a different risk profile than one scoped to read-only access with every action logged.
Most serious agent deployments converge on similar guardrails: scoped capabilities (what tools, what data), spending or rate limits, human approval for consequential actions, and provenance (which model, what confidence, what it cited) attached to whatever the agent produces. Without those, "agentic" software is a liability wearing a feature's name.
AI Agents, in the product
Aanty treats agents as native principals — the same identity, audit and scoping model as a human account, not a bot bolted onto a user table. Every agent runs on a capability ladder (read-only → draft → low-risk write → consequential write with mandatory approval) and every agent message carries model, version, confidence and citations.
Related terms and pages
See AI Agents in a real workspace
Bring a channel from wherever your team works today. In fifteen minutes we'll show what ai agents looks like on a real conversation, not a slide.