Recon, Then Wreck the Roadblocks: How Recon‑Act Turns Web Stumbles into Tools
Thesis: The next leap in practical web agents isn’t bigger models or deeper search trees—it’s a tight loop that learns by failing well. Recon‑Act’s two‑team architecture (Reconnaissance → Action) turns mistakes into generalized tools and feeds them back into execution. That’s not just a benchmark trick; it’s an operating system for enterprise‑grade automation. Why this matters (for operators, not just researchers) Most “browser LLMs” still thrash on real websites: ambiguous DOMs, mixed text‑image signals, fragile flows, and long horizons. Recon‑Act reframes the problem: when progress stalls, stop trying harder—learn smarter. It does three things companies can copy tomorrow: ...