AI Agents & Automation

An AI agent receives a goal, then plans, makes decisions, calls tools and carries a multi-step process through to the end without someone holding its hand at every turn. Put several of them together and you get an agent swarm, a multi-agent system where specialized agents take on roles and talk to each other over protocols like MCP (Model Context Protocol) and A2A.

Turning that idea into a system that actually runs takes workflow automation. We build with n8n and, depending on the case, with frameworks like LangGraph, CrewAI or AutoGen, wiring agents to your CRM, email, databases and APIs so the flows stay reliable and repeatable.

There are enough demos out there; what matters is systems that hold up in daily use. We explain honestly which architecture fits which case (ReAct, planner-executor, reflection), where agents genuinely save time in marketing, sales, support and content operations, and where autonomous systems run into their limits.

Every approach gets thought through along the EU AI Act and GDPR, with data sovereignty, human-in-the-loop controls and an audit-proof setup that produces evidence instead of hiding the effort. In the European market, compliance decides whether a project is even allowed to go into production. Whether you are planning a first pilot or want to grow existing automations into an orchestrated agent system, as a Vienna-based agency we walk the path from architecture to an agent that runs compliantly in regular operation, with concrete guides, plain explanations of the tools named above and a realistic take on effort and operation.