The Mentiora Blog

In this guest post, Flo's VP of Engineering Andrei Varanovich argues that the real challenge in AI agents isn't intelligence — it's engineering discipline. Drawing on Google's ML Test Score, he introduces an 'Agent Test Score' framework to help teams ship agents that don't just demo well, but hold up in production.

Prompt optimization plays a key role in improving the performance of AI assistants. This article reviews manual and automatic optimization methods, explains how to evaluate prompts reliably, and shows how practitioners can compare approaches to identify high-quality prompts.

Agent quality extends beyond accuracy. At scale, subtle behavioral issues can erode trust long before metrics signal a problem. Mentiora's Insights transform evaluation into a disciplined loop for understanding and improving agent behavior.

Moving from a demo to a reliable product is a bumpy road. Learn how Mentiora powers successful LLM workflows by anchoring development in clear specs, turning evaluation into improvement, and embedding governance directly into the shipping rhythm.

Prompting seems simple, but it steers how AI agents behave. In production, prompts aren't one-off crafts, but they're living system artifacts that need versioning and upkeep. Mentiora helps teams manage and optimize prompts, delivering measurable, repeatable performance gains at scale.

Your agent can look great on paper yet still miss the mark—failing to close tickets, earn trust, or drive outcomes. Learn how Mentiora closes the gap by measuring what stakeholders care about and optimizing agents against those signals.