I'm building profile-guided optimization for AI agents

A pattern I keep seeing in agent systems is that model selection is mostly static. You pick a strong model for the agent, maybe manually downgrade a few steps, and hope the cost/quality tradeoff holds in production. I'm building Agent-PGO around a different approach. It profiles real executions at the node level, measures where the cost and latency actually go, then tests cheaper model substitutions against an eval suite. A substitution only survives if it stays inside explicit quality bounds. So something like a formatter or extractor might move to a cheaper model, while a reasoning-heavy node stays on the stronger one. The interesting part isn't finding the cheapest model. It's finding the cheapest execution plan that still passes the workload. The landing page and optimization studio are now working. Backend V1 is currently being built. I'll share the profiling/optimizer design and real benchmark results as it becomes usable.