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The Problem: Teams waste countless hours on repetitive data tasks—extracting numbers from Excel files, reformatting reports, cross-referencing spreadsheets. You hire people or write brittle scripts that break the moment the format changes. Traditional automation fails because it can't adapt, and AI agents today fail silently—you don't know they got it wrong until it's too late. What I'm Building: An AI automation platform that doesn't just execute tasks—it validates its own work and learns from mistakes. Instead of hoping your automation got it right, my system: Checks its own answers against expected outcomes using an evaluator agent Retries with feedback when it gets things wrong, iteratively improving until it succeeds Builds new tools on the fly when existing capabilities aren't enough Shows you exactly what happened with every tool call and decision traced Why It Matters: You get automation that's actually reliable. When something goes wrong, the system debugs itself and tries again—just like you'd tell a junior employee to "check your work and fix it." No more silent failures. No more brittle scripts breaking on edge cases. It's automation that thinks, validates, and improves—not just executes blindly.

Built With

• anthro
• typescript