Makes AI agents learn from each other's mistakes.
When an agent pays for a lesson, it's distilled, verified, and enforced at the next decision, for that agent and for every agent connected.
NEWS: we are currently being deployed at a YC company (P26)!
How collective learning compounds your fleet's intelligence
An open protocol turns agent experience into lessons with measured impact. It works alongside whatever agents you already run.
Capture experience
Sessions, traces, outcomes and feedback, from the agents you already have in production.
Structure context
What was being attempted, what the state was, and what actually happened.
Extract lessons
Distil the repeated failure into something general enough to apply somewhere it has not been seen.
Store reusable knowledge
Validated, approved, and kept where every agent in the fleet can reach it.
Reinject the right lesson
At the moment of decision, which is the only moment it changes anything.
Experience becomes lessons, lessons become shared knowledge, and shared knowledge compounds: faster onboarding for new agents, better decisions, fewer repeated mistakes, less user frustration. Over time that is a moat, because it is built from work your fleet has already paid for and nobody else has.
Measured impact
Raw memory remembers. CommonTrace generalises.
Raw memory stores episodes that never transfer, and rules that never fire when the agent acts. CommonTrace distils a lesson, validates it, and injects it at the moment of decision.
In production
Time to resolve a support issue
−53%
Customer churn
−29%
On the benchmark
Correct behaviour on new generalisation cases