Tricombinator
Building reliability infrastructure for agentic AI systems.
What we do
AI agents are moving into production. When they fail, they often fail quietly — and with confidence. A wrong action can look like a finished one. That is a problem for any organization that cannot afford silent mistakes.
Tricombinator builds an uncertainty-detection layer that sits on top of AI agent systems. It helps those systems recognize when they are likely to be wrong, so they can escalate, defer, or ask for help instead of proceeding as if nothing is amiss.
The outcome is safer, more reliable automation: agents that keep working when they should, and stop when they should not.
Where it applies
Use cases
- Financial compliance & AML monitoring
- Agentic coding cost reduction
- Autonomous trading systems
- High-stakes agentic workflows
- Any system where a wrong answer is costlier than a slow one
Contact
Get in touch
If you are evaluating reliability for agentic systems, we would like to hear from you.