Observability Limits: From Volume to Cardinality

We often interact with AI on the surface through chatbots, recommendations, and smart assistants. But what powers all that intelligence beneath the hood? If you’ve ever wondered what really makes AI systems tick, here’s a simple breakdown of the 7 core layers of AI architecture — from raw data to real-world deployment.

Meant to post this yesterday but caught in too many meetings. Adding more agents in certain context can degrade accuracy and cost 2-3x more. This talks about some interesting testing from Google and MIT on Single AI Agent vs Multi-Agent architecture decisions. Just found it incredibly interesting and thought it deserved simplifying down. https://lnkd.in/dzS23Her

The reason so many AI pilots stall isn't the model—it's the data foundation. Watch the Techstrong TV interview with Boris Bialek, VP and Global Field CTO for Industries at MongoDB, highlighting how AI success doesn't come from better prompts, but better architecture. Boris shares real-world examples (from healthcare to retail) illustrating how to shift focus from "model obsession" to: -Delivering relevant context at scale -Ensuring data readiness and governance -Solving for latency in production Practical, grounded, and worth your time. Watch the interview: https://lnkd.in/e2PZasC5

AI is only as good as the context you give it. In our latest blog, we explain how Context Engineering connects enterprise architecture, data, and semantics to create AI you can actually trust. You’ll learn: • How EA data grounds AI models in reality • Why stronger context reduces hallucinations • What it takes to build explainable, enterprise-ready AI Read the full blog: https://hubs.li/Q03VbDBZ0

I co-authored a white paper on hybrid AI architecture in capital markets. The main conclusion is straightforward: hybrid isn’t a compromise. It’s a necessity. Capital markets AI needs to meet several requirements at the same time: low latency, strict data residency, elastic compute, and strong governance. In practice, cloud-only or on-prem alone rarely satisfy all four. That’s why hybrid architectures are increasingly used,not as a halfway option, but as a practical way to balance performance, regulation, and scale in real production environments. If you’re interested in the white paper, send me a message and I’ll share it.

Many of our clients operate in highly regulated industries, from capital markets and insurance to healthcare and beyond. Working in these environments has shaped how we think about AI from the ground up. In these settings, hybrid AI architectures matter in practice. They’re not a theoretical preference, but a response to real constraints around data residency, latency, security, and governance.

Sharing this with my network, there will be some great takeaways, especially as AI-powered applications are pushing traditional databases beyond what they were designed for. In this executive briefing, Databricks VP of Product Shanku Niyogi and product leaders share how Lakebase introduces a new transactional foundation built for the AI era. What you’ll learn: - Why intelligent applications strain traditional architectures - How Lakebase changes the equation - How unifying data, applications, and AI reduces complexity - How application architectures are evolving

Turning strategy into shipped value requires more than ideas. It takes secure-by-design architectures, governable data, and production-grade AI capable of moving from intent to measurable outcomes. At iNBest, we focus on building with rigor so organizations can scale with confidence—without compromising security, governance, or operational resilience. This is how strategy becomes execution, and execution becomes impact. Discover how we do it at iNBest www.inbest.cloud

When I ask AI to implement a feature, I do not just describe what I want. I share the decisions that shaped the system. ADRs give AI the "why" behind architecture, preventing it from re-litigating issues the team already decided.

When I ask AI to implement a feature, I do not just describe what I want. I share the decisions that shaped the system. ADRs give AI the "why" behind architecture, preventing it from re-litigating issues the team already decided.