Distributional

Big update on the new direction of Distributional from @DrScottClark about the pivot to @TalariaSci

DBNL helps you make sense of the patterns in your AI agent behavior, detecting where there are deviations and then providing automated recommendations so that you can build an application that behaves like you want it to.

Get started: distributional.com

Kick off your AI analytics flywheel with DBNL. DBNL transforms traces into actionable insights, ensuring teams can continuously fix and improve their agents with the right evidence in production: dbnl.io/cPRvZf

Is your AI agent actually calling a tool when it claims to? Scott Clark explains one of the most common anti-patterns he sees with multiple models today, where the agent gets "lazy" and will say it called a tool but the traces prove otherwise.

Learn more about how DBNL's analytics helps teams catch dark patterns: dbnl.io/iMRLJz

Every day, DBNL users get fresh insights on their most recent production data. These insights drive the development of new offline metrics to guide reward functions, evals, or context for agents. They can be used to generate new test cases that are run pre-production on agents or in a continuous “integration testing” style setting. And they create new metrics to be monitored real time by more traditional observability tooling.

Learn more about how DBNL analytics generate actionable insights: dbnl.io/cPRvZf