Building AI Agents with LLMs and Custom ML Models: The Blueprint for Enterprise-Ready Autonomy

Introduction: The Rise of Hybrid AI Agents AI agents are rapidly emerging as the next abstraction layer for enterprise artificial intelligence. Rather than interacting with models directly, organizations are beginning to deploy systems that can perceive inputs, reason over context, and take action with limited human intervention. Large language models have accelerated this shift dramatically,... Continue Reading →

January 21, 2026 0

The Transformer Architecture: Foundations, Engineering Trade-Offs, and Real-World Deployment at Scale

I know that many resources explain this architecture, including the pivotal paper Attention Is All You Need. I wanted to write this to cement the concepts in my mind. This architecture is what is driving the current AI revolution, so it is essential to have a good grasp of the ideas. Since its introduction in... Continue Reading →

January 16, 2026 0

AI Governance and AI Observability in the Microsoft Stack: Building AI You Can Trust

Artificial intelligence has entered a phase where models are no longer the center of gravity, behaviors are. We’re deploying systems that reason, retrieve, act, and adapt in real time. They generate content, make decisions, and increasingly operate as semi‑autonomous agents woven into everyday business processes. This shift has opened extraordinary opportunities, but it has also... Continue Reading →

January 9, 2026 0

Becoming a Frontier Firm: How to Scale AI with Trust and Speed

A new year, a new article. A quiet shift is happening in the business world, and it’s reshaping what it means to be a modern leader. While many companies are still experimenting with AI in pockets of the organization, a new class of businesses has already moved far ahead. These are the frontier firms, organizations... Continue Reading →

January 5, 2026 0

Interpreting Machine Learning Results: Beyond the Accuracy Score

Machine learning models have transformed industries, from diagnosing diseases to predicting customer behavior. However, building a robust model is just half the battle. The real value comes from understanding what your ML results mean and how to communicate them responsibly. For data scientists, ML engineers, and tech professionals alike, interpreting machine learning results is as... Continue Reading →

December 22, 2025 0