AI Observability is Expensive Voyeurism
The observability market is selling you dashboards to watch your AI fail in high resolution. What you need is controllability.
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Insights on AI trust
Practical perspectives on agentic systems, data architecture, and building AI that works in production.
Posts - Page 3
Your Data Team is Building an AI Graveyard
Every transformation in your data pipeline destroys information AI needs. Traditional data engineering is a lossy compression algorithm.
Read more →Multi-Agent is This Decade's Microservices Mistake
The multi-agent hype will collapse. We learned this lesson with microservices. Distributed systems are hard.
Read more →The AI POC Trap: Why Your Demo Worked and Production Won't
Your agentic AI POC impressed leadership. Then you tried to scale it. Here's why demos deceive - and what production actually requires.
Read more →Foundation Models Are a Commodity. Act Accordingly.
Everyone's agonizing over Claude vs GPT vs Gemini. It doesn't matter. The differentiation is moving up the stack.
Read more →The LLM Evaluation Maturity Model: Where Does Your Team Actually Stand?
A six-level framework for assessing how your organization evaluates LLM outputs. From 'it looks right' to continuous evaluation pipelines with regression detection.
Read more →The EU AI Act Is Here: What Financial Services Firms Need to Know
A practical guide to EU AI Act compliance for banks, insurers, and investment firms. What's required, what's high-risk, and how to prepare before enforcement begins.
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