An Intellyx Brain Candy Update
Distributional runs continuous post-training and post-deployment discovery and review of agentic AI applications based on log analysis and scaled mocks in order to gauge their scalability and resilience, in order to provide feedback to design and recommend improvements.
This sounds like quite a stretch from the company we last briefed in 2024 that was testing AI agents prior to deployment. However, once you put non-deterministic agents into production, they interact with users, systems, and other models and agents, and then things get too freaky and expensive to test. It turns out it is much more useful to observe agents and react to their unexpected behavior in the context of everything they interact with, than to try to validate expected agent behaviors.
In many aspects, the user experience here looks similar to other leading observability platforms which always demo impressively. You are looking at logs and metrics, call paths and user journeys, but only the very specific ones that are indicative of the production performance footprint of agents moving among clusters, through APIs, and in user prompts and coding assistants.
An expert LLM that understands token cost models, agent call times, and esoteric stuff like hyperparameters optimization works alongside the human manager to segment the data for further relevance, and recommend and/or automatically make updates, depending on policy settings.
Oh yeah, and Distributional comes in an open distribution that can be freely installed in your VPC or onto a scalable Kubernetes cluster.
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