Short answer: An AI agent for an SMB is a scoped, governed worker that completes one job end to end. It might triage inbound leads, capture invoices into Odoo, or deflect support tickets, all by calling your tools through connectors. The ones that work stay narrow, get measured against an eval set, and are priced on the outcome. The ones that don't are the open-ended ones trying to be "autonomous".
What's the difference between an AI agent and a chatbot?
A chatbot answers. An agent acts. It can read your data, call your APIs (often via MCP), work through a multi-step task, and then stop when it's done. That's the real shift happening across the market: you go from operating the software yourself to handing the job to a worker that does it.
Which jobs are worth giving to an agent first?
Pick one painful, measurable workflow:
- Lead triage: qualify inbound leads and route them into your CRM.
- Invoice / document capture: pull the data out of PDFs and into Odoo.
- Ticket deflection: answer the common support questions and escalate the rest.
Prove it there first, then expand. "AI transformation" bought as one big bang almost always stalls.
How do you keep an agent reliable?
It comes down to three habits. Scope it to one job. Guardrail it with tool permissions, dry-run modes, and human-in-the-loop review wherever a mistake would actually cost you. And measure it against an eval set built from real cases. Gartner expects a large share of open-ended agent projects to get cancelled, and staying narrow and measured is how you stay on the right side of that trend.
How are AI agents priced?
We're piloting outcome-based pricing, tied to hours saved or items processed instead of seats or tokens. That way what you pay tracks the value the agent actually creates. With model costs still falling, it's the pricing that holds up.
Don't go shopping for "an AI agent". Pick one job, make it reliable, and track the one number that tells you it's working.