Lucidic AI: Weights & Biases for AI Agents
Summary
Instead of sifting through logs, you get a visual breakdownsearchable workflow replays, decision nodes with outcome probabilities, step-by-step agent action trajectories, and side-by-side simulation comparisons.When we started building agents, it seemed trivial: Call GPT a few times, string together some logic, and it worksuntil it doesnt.The moment you become complex, its a disaster. Intelligently visualize thousands of complete workflow trajectories at once, showing success rates, failure points, and decision paths for faster debugging. Abhinav (CEO) has worked as a researcher at the Stanford AI Lab, a quant at Citadel and SIG, and a software engineer at Apple. Andy (CTO) qualified to represent Stanford (one of three) at the North American Championship for the largest collegiate programming competition in the world (ICPC). Jeremy (Chief Scientist) is a dedicated machine learning researcher with years of experience working on state-of-the-art models at Steel Dynamics (F500) and DRW.