AI agents in practice: self-learning, knowledge bases, and why fewer agents is better
Building AI agents sounds fun until you actually build one. Then a different set of problems shows up — ones nobody writes about. Here is what I have learned running agent systems in production: self-improvement conflicts with git, most knowledge bases hit a wall sooner than expected, and adding more agents almost never helps. The self-improvement problem One of the selling points of agents like Hermes is that they can self-reflect and improve — updating their own rules based on experience....