🧠 The hidden constraint in agent research: economics, not ideas Recent reactions around systems like Hermes-style agents are predictable: strong feedback loops, self-improving behavior, memory accumulation, tool chaining — and a consistent narrative of “it gets better over time”. This class of systems is becoming the default template for modern agents. But something important is missing from most discussions. \--- \## ⚙️ 1. The real pattern: feedback-first agents Systems like Hermes follow a common structure: \- LLM as a policy engine \- persistent memory \- tool execution layer \- post-hoc correction loop \- continuous skill refinement This produces an intuitive result: \> performance improves through interaction, not through structural constraints It works well on demos, benchmarks, and iterative tasks. And that’s exactly why it dominates current discourse. \--- \## 📊 2. Why this direction dominates It’s not just an architectural choice — it’s an \*\*economic one\*\*.…