Together with my colleague Marius Högger, I wrote an article for the online magazine Netzwoche on AI agents as enterprise assistants. The piece examines how generative models like ChatGPT become far more capable when wrapped in an agent architecture that gives them tools, memory, and a defined role.
An AI agent is a virtual worker with a character profile and a tool set. It can draft social media posts, answer internal knowledge queries, or orchestrate multi-step business workflows. The key distinction from a bare chatbot is autonomy: an agent decides which tools to invoke and when to escalate.
We defined three levels. Basic agents handle constrained tasks using preset parameters and style guides. Intermediate agents access internal knowledge bases and the web, choosing sources autonomously. Advanced agents interact with external applications and databases, iterating toward solutions for complex, multi-step problems.
AI agents break down knowledge silos by exposing enterprise data through a single conversational interface, governed by role-based access controls and security protocols. Automation of routine processes frees teams for strategic work.
The article made the case that AI agents are not a speculative technology but a deployable one, provided organizations invest in proper access controls, monitoring, and incremental rollout. My work at bbv Software Services focused on exactly this kind of implementation.
1. Basic AI agents for simple tasks with preset parameters, 2. Intermediate AI agents with access to internal knowledge bases and the internet, 3. Advanced AI agents with a broad tool palette and the ability to solve complex problems iteratively.
AI agents enable breaking down knowledge silos, centralized data access, process automation, and increased efficiency. They allow teams to focus on strategic tasks while routine work is automated.
Advanced AI agents can interact with various external applications and databases, solve complex problems iteratively, and have access to a broad range of tools. They operate more autonomously and can handle more demanding tasks than basic agents.
AI agents feature role-based access controls and security protocols to protect sensitive data. They enable controlled access to enterprise data through a central interface while maintaining data security.
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Copyright 2026 - Joel P. Barmettler