Glossary
Automation, AI, agents, the AI Act — explained properly.
Four chapters that explain the vocabulary of agentic automation in depth — with real-world examples and drawings. Written for decision-makers and business teams, not researchers.
Automation & RPA
Where it all began: from scripts to software robots. The vocabulary of classic process automation — and its built-in limit.
8 termsRead the chapter 02AI & Machine Learning
From learning models to language models: the vocabulary behind the technology that taught automation to understand.
9 termsRead the chapter 03Agentic AI
When AI stops just answering and starts doing the work: agents, their tools, their loop — and the controls that make them enterprise-ready.
9 termsRead the chapter 04EU AI Act
Europe's rulebook for AI: risk classes, obligations, deadlines — and what actually matters for agentic systems in the enterprise.
9 termsRead the chapterThe evolution
How we got to agents.
The history of automation is a history of growing reach. Scripts and interfaces connected the systems built for it. RPA (from around 2015) broke through that limit: software robots operated any application through its user interface — fast to deploy, but rigid, because a bot only knows the script it was given. Machine learning (in broad use from around 2018) brought understanding into the flow: reading documents, classifying cases, estimating risk. Straight-through rates rose — yet every real exception still landed with a human, because the models lacked language and context.
That is exactly what large language models changed (from 2022): software could suddenly read, reason and write. The final step is the one to agents (from around 2024): LLMs get tools to act inside real systems, and a loop in which they perceive, plan, act and verify themselves. For the first time, software owns outcomes instead of steps — and that is where the real work begins: identity, limits, oversight and traceability, because the capacity to act without governance is not progress but risk.