Stop iterating on vibes.
Pebl turns your traces into actionable insights so you can start building on evidence, not vibes.
AI agents are reshaping the way we interact with the world. Increasingly, we start and finish tasks by prompt, collaborating with agents that gather context, reason, and act on our behalf.
Every agent interaction generates a trace, which is a living replay of how a user and model work together. Inside these traces are clues into how users behave, where agents do well, and where they don't.
Today, these traces live in spreadsheet-like dashboards built for debugging one request at a time. Teams struggle to answer the most basic questions: How did my agents do today? Where do they struggle? What should I build next?
Answering these questions is still deeply manual. Engineers chase issues across Slack threads. PMs decide what to build based on vibes. The real signals remain buried in mounds of traces in interfaces that weren't designed to uncover them.
Pebl reimagines the way teams interact with their traces.
Instead of scrolling through logs, you explore a map. Start broad, with clusters of traces grouped by shared intent. Click into one to see real examples of user behavior. To dive deeper, chat directly with Pebl to surface patterns, failures, and insights across traces. And when you find something worth tracking, spin up an eval with a single prompt.
We believe building the best agents starts with a solid understanding of how they behave in the wild. We're building the context layer teams need to gain this foundation.
We're looking for small teams interested in using this. If you're down to give it a shot, drop your email here: