The Internet of Things (IoT) has long since made its way out of pure technology showcases and into productive use in mid-sized companies. Machines, equipment, and buildings now continuously deliver data – but the real challenge no longer lies in capturing the data, but in processing and integrating it meaningfully.
Most AI assistants deployed in enterprises today follow a simple pattern: a request comes in, a response goes out — and with the next chat, everything starts over from scratch. Hermes Agent, an open-source project from Nous Research, breaks with this pattern. Rather than running as a wrapper around a cloud API, Hermes installs itself persistently on your own server, retains context across sessions, and builds up a growing repertoire of reusable capabilities over time. For companies seriously evaluating local AI agents – as already described in our post on Ollama – this deserves a closer look.
Agentic AI describes AI systems that do not merely respond to individual inputs, but independently plan and execute multi-step tasks while flexibly adapting to changing conditions. Unlike traditional AI assistants, which receive a request and deliver a response, agentic systems act in a goal-oriented manner: they break complex tasks down into sub-steps, select appropriate tools, execute them, and verify the results – all without human intervention at every individual step.
The term derives from the English word “agent” (one who acts, a representative). An AI agent is a system that perceives, decides, and acts within its environment – continuously and aligned toward an overarching goal.