Home / Understanding agentic AI
Understanding agentic AI
AI agent vs chatbot: what is the difference for the enterprise?
A chatbot answers, an AI agent acts. Here is how to tell a conversational assistant, an autonomous AI agent and a multi-agent system apart, and how to deploy them in the enterprise.
Definitions
A chatbot (or conversational agent) answers a question within a conversation. It does not carry a task through to the end by itself.
An autonomous AI agent chains several steps to reach a goal: it plans, uses tools (code repository, tests, tickets, databases) and produces a deliverable. Agentic AI refers to this ability to act, under human control.
A multi-agent system brings together several specialised agents that cooperate. In HALO, five agents (PO, Architect, UI/UX, TechLead/Dev, QA) produce, and Brain, at the centre, listens to every exchange.
| Criterion | Chatbot | Autonomous AI agent | Multi-agent system (HALO) |
|---|---|---|---|
| Role | Answer | Perform a task | Deliver a project end to end |
| Tools | None or few | Repository, tests, APIs | One specialised agent per role |
| Human decision | At every message | At the end | At every gate, by an identified human |
| Memory | Conversation | Task | Rules captured by Brain, versioned |
How do you deploy AI agents in an enterprise?
- Pick a real, bounded change, described on one page.
- Install the platform inside your perimeter (on-premises or private cloud).
- Connect your repository and models, with limited rights.
- Let your teams judge the result on real code, then extend.
That is the principle of HALO's 48-hour proof. For the security side, see
Frequently asked questions
Does an AI agent replace teams?
No. In HALO the agents produce, but three human roles (PO, Team Lead, tester) decide at every gate, and responsibility for going to production stays named.
The proof on your code, in 48 hours.
No three-month pilot: a real change, delivered in your repository, judged by your team.
Request the 48-hour proof