The stochastic challengeTraditional software is predictable: Input A plus function B always equals output C. This determinism allows engineers to develop robust tests. On the other hand, generative AI is stochastic and unpredictable. The exact same prompt often yields different results on Monday versus Tuesday, breaking the traditional unit testing that engineers know and love.To ship enterprise-ready AI, engineers cannot rely on mere “vibe checks” that pass today but fail when customers use the product. Product builders need to adopt a new infrastructure layer: The AI Evaluation Stack.This framework is informed by my extensive experience shipping AI products for Fortune 500 enterprise customers in high-stakes industries, where “hallucination” is not funny — it’s a huge complia [...]
Traditional software governance often uses static compliance checklists, quarterly audits and after-the-fact reviews. But this method can't keep up with AI systems that change in real time. A mac [...]
OpenClaw has become one of the most widely adopted agentic frameworks, but it has yet to prove itself at enterprise scale. Agents need real credentials — API keys, OAuth tokens, service accounts — [...]
“When you get a demo and something works 90% of the time, that’s just the first nine.” — Andrej KarpathyThe “March of Nines” frames a common production reality: You can reach the first 90% [...]
A retrieval-augmented generation (RAG) system is built to answer strictly from the documents it retrieves. But when engineers optimize these AI pipelines end-to-end, the reader module can learn a shor [...]
A CISO who sees a low CVE count and deprioritizes prompt injection is reading the scoreboard wrong. Prompt injection has held the No. 1 spot on the OWASP Top 10 for LLM Applications for three consecut [...]
Agents are the trendiest topic in AI today — and with good reason. Taking gen AI out of the protected sandbox of the chat interface and allowing it to act directly on the world represents a leap for [...]