Lucidic AI
Weights & Biases for AI Agents
TL;DR: Lucidic is an AI agent analytics platform that maps every step of your agent's workflow and simulates their performance at scale, cutting iteration time from weeks to minutes. Instead of sifting through logs, you get a visual breakdown—searchable workflow replays, decision nodes with outcome probabilities, step-by-step agent action trajectories, and side-by-side simulation comparisons.
The Problem: Building Good Agents is Hard
When we started building agents, it seemed trivial: Call GPT a few times, string together some logic, and it works—until it doesn’t.
The moment you become complex, it’s a disaster. One day, your agent is working fine; the next, it’s breaking for no reason. With misaligned reasoning, brittle logic chains, unclear failure cases, or silent performance degradation, you end up spending hours rerunning prompts, tweaking edge cases, and wondering why something that should work just doesn’t.
And don’t even get me started on debugging. Running the same prompt over and over again, hoping this time it’ll behave the way you want? That’s not a strategy—it’s a nightmare.
Our Solution
Lucidic changes the game. No more late nights staring at a terminal, guessing what went wrong. See exactly how your agent’s brain works—replay actions step by step, inspect decision trees, and visualize internal reasoning in real time. Modify prompts and logic with instant, structured feedback so you’re never debugging in the dark again.
No more trial-and-error fixes—just clear, interactive breakdowns of why your agent fails and how to fix it. Test at scale, compare behaviors side by side and optimize performance before deployment. Intelligently visualize thousands of complete workflow trajectories at once, showing success rates, failure points, and decision paths for faster debugging.
Get Started
- Sign up for our private beta: lucidic.ai
- Book a demo with us here
- If you know anyone who is building agents, send ‘em over: team@lucidic.ai
🧠Stop watching agents run. Start seeing how they think.
The Team
Abhinav, Andy, and Jeremy met while playing Super Smash Bros freshman year at Stanford. Since then, they’ve worked on multiple deep learning research projects together. 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.
