Why AI Agents Need to Know When to Quit
As AI agents increasingly work in loops—generating an answer, checking it, refining it, and repeating—one of the hardest problems isn't getting them started, it's getting them to stop. A16z's piece digs into the design patterns behind convergence: how do you build a system that knows a solution is 'good enough' rather than looping forever, burning compute, or quitting too early with a half-baked result?
The piece walks through techniques like setting explicit success criteria, using confidence scores, capping iteration counts, and having a separate evaluator step judge progress rather than trusting the agent's own self-assessment. It frames this as a core architecture decision for anyone building agentic systems, not just a minor implementation detail.
This matters increasingly as more products ship 'agentic' features that chain multiple LLM calls together.