Agentic AI: automate your workflows with AI agents

Agentic AI refers to systems that receive a goal rather than a question, choose for themselves the steps and tools needed to reach it, check their result and start again if needed. Unlike a conversational assistant, an agent does not merely answer: it acts in your systems.

Classic automation runs a fixed, predefined sequence. It is more robust and cheaper when the process is fully deterministic. An AI agent becomes relevant when you need to interpret language, unstructured documents, or choose between several paths depending on context.

Three items: design and development of a production-ready agent, typically between €2,000 and €5,000 at ATG because the platform already provides connectors, ingestion, permissions and monitoring; runtime token cost, which varies widely with the task and must be estimated case by case; and maintenance, roughly one review per quarter. At ATG the whole thing is fixed and known before go-live.

MCP (Model Context Protocol) is a standard that lets you expose a tool once so that any agent can use it. In practice it removes the need to rebuild every integration for every agent, and clearly shortens time to production.

Under three conditions: a narrow and explicit perimeter, permissions inherited from the user or from a bounded service account, and complete logging of every decision and every tool call. On irreversible actions, we keep a human validation step.

A few days for a simple automation built on standard connectors. A few weeks if custom connectors have to be created, if the business rule needs formalising, or if the process touches irreversible actions requiring validation.