a16z's Yoko Li on Making AI Agent Loops Converge and Know When to Stop
stuffyokodraws · x · 2026-08-07
Yoko Li from a16z explores a core challenge in designing AI agent loops: how to make the system recognize when a task is complete and stop running.
The article points out that while a model can revise its output infinitely, "done" is rarely an objective property of the work itself. Instead, it is a judgment produced by the surrounding system, relying on external signals like passing tests, team approvals, or deadlines. For an agent loop to converge effectively, it depends on two axes:
- Editability of the artifact: How easily the output can be modified.
- Verifiability of the result: Whether the outcome can be objectively verified.
Coding became the first successful agent loop because code is both highly editable and equipped with strong verifiers (like tests). Conversely, open-ended image generation sits at the bottom end of these axes, as the system struggles to repair specific decisions or verify if the result is closer to the user's intent.
Related event: a16z Explores Termination Conditions for AI Agent Loops(4 posts)→
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