Evals, And How They Help Build Good AI Agents For Enterprises​

Rohit Patel, Founder and CEO of ndoc.ai.

Every executive today feels the urgency to deploy generative AI. Boardrooms want autonomous agents that can resolve customer inquiries, synthesize contracts and automate multistep operational workflows. Early prototypes look dazzling: Within hours, an engineering team can connect a foundation model to internal APIs and produce a demo that feels like magic.​

Yet a familiar pattern has emerged across industries: Promising pilots stall before reaching production, or stumble once exposed to real users. Up to 95% of enterprise AI programs fail to deliver measurable value. While leaders often blame hallucinations or edge cases, RAND Corporation research shows the real root cause lies in how we build, with failure rates doubling those of conventional IT projects.

Agentic Performance: The Measurement Challenge​

Why is measuring AI agent performance so difficult? Unlike traditional enterprise software, evaluating agentic systems presents two fundamental hurdles:​

First, AI is non-deterministic. Traditional software is deterministic: Developers write explicit rules and unit tests, asserting that input X always yields exact output Y. Generative AI breaks this paradigm. At its core, an LLM is an intuition machine calculating statistical probabilities. Because agents produce open-ended language and multistep decisions, you cannot check for an exact match. A customer service response can be phrased in dozens of valid ways while remaining accurate and compliant, yet it can also sound completely fluent while being subtly incorrect.​

Because of these twin challenges, too many organizations fall back on “vibe checks.” Teams test a few cherry-picked prompts, nod at impressive demos and green-light deployment. In an enterprise environment, launching mission-critical software on subjective impressions invites severe brand, legal and operational risks.