Every "AI agent" product on the market is making one of two architectural bets, and the founders usually can't articulate which. The bet decides whether your agent costs cents or dollars per run , whether it works the same way twice, and whether it will still be running a year from now. It's worth naming. The Two Camps Fat harness, agentic. The LLM decides every step at runtime. Every run is a fresh plan. Every plan costs tokens. Every step is an opportunity for the model to go somewhere new. Examples: Claude Code, Cursor, Copilot agents (open ended coding work) LangGraph (reasoning over a graph) CrewAI (agents organized by role, +280% adoption in 2025) AutoGPT (autonomous loops) OpenAI's AgentKit Thin harness, deterministic. The LLM designs the pipeline once, at build time. Then code runs forever . The model gets called only for the specific steps that actually need judgment. The trigger is deterministic. The data sources are scoped. The output goes somewhere predefined.…