Agentic Rush

Miracles wear off quickly these days. LLM chats are meanwhile everyday business , so we need new toys - the next rush is Agentic AI. Most uses cases and demos we see in the agentic AI world today are simple sequences of steps, mostly broken down to avoid model overload. This is nice but not very impressive. Why all the craze about agentic AI systems?

💡 The novel promise is that complex processes will not be streamlined to make human work more efficient but will replace human work. This happens by overcoming the "digital barrier", which means, AI competes not with other IT systems about budgets and resources but with the human work budgets, which are a much bigger cost factor in companies today. Helen Edwards of Artificiality wrote an interesting post about this shift. That is where the new gold rush has its origins.

So, what's happening, and what can we expect?

💡 In the heart of most modern IT systems of any type is the coded and structured prescription of outcomes to expect. Quick but deterministic. All this is built on a set of digital primitives which start at the binary state of a computer memory cell, combined with a calculater of these states. To run today's IT ecosystems, we built massive stacks and organizational patterns around this binarity. Satya Nadella targeted those stacks as obsolete when he said that SaaS is dead and needs to be replaced now within the new paradigm of "artificial thoughts".

💡 Agentic AI Systems are yet in their infancy but we see the components emerging. A new operating system is being built. The language spoken is a pidgin of natural language and semi-structural constraints like the pleading "give me JSON output" prompt, or emerging standards how agents call tools (OpenAI's Function Calling), or how all those agents are to be dispatched and synced (e.g. fetch.ai). Agents are the process workers, which need working memory, long-term memory, processing resources (tool access), LLM model access, and communication capabilities. These are the primitives which are under construction in the moment in an exploding open source-driven startup market (many of those relying on the emerging libraries of LangChain and LlamaIndex) , and of course from the big players OpenAI, Google, AWS, Anthropic.

💡 from a development and operational point of view, this new paradigm is "wasteful" because the hyperautomation carrier is natural language which is not optimized for machine processing. But the mimikry of human behaviour seems currently to be the best bet for getting a grip on the increasing complexity which can not be managed by classic digital technology alone.

We will see in the next years how that bet paid off.