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 wrote a blog post about yet. This is a summary of a conversation between developers actively running agent systems in production or near-production. The topics: self-improvement conflicts with git, what to use for a knowledge base, Andrej Karpathy’s Obsidian approach, and why adding more agents rarely 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....