Small ML Sub-Problems Inside Agents
When you build an agent, you’re not building one system. You’re building several small ML problems composed together, each one making a quality judgment, each one capable of failing silently. I learned this while building a deep research agent. At a high level, the system takes a question, breaks it into smaller research questions, searches for sources, extracts claims, and keeps a structured record of what it has found. A planner reads that record after every search and decides whether to investigate a new question, challenge an existing claim, or stop. ...