Everyone is asking: β’ Which agent(s) should we build? β’ Which framework and tools should we use? β’ Which model(s) should we choose? After spending the past several months building and studying enterprise AI architectures, Iβve come to believe those are the wrong first questions. The biggest challenge isnβt
For months, I obsessed over prompts, tools, and reasoning loops. Then I hit production β and the reality set in. Hereβs what finally became clear to me: Once an agent leaves the demo phase, it stops being a reasoning problem. It becomes a systems engineering problem. Three infrastructure realities determine
Your AI strategy might have a hidden million-dollar leak. I previously described the Model Context Protocol (MCP) as the "Nervous System" for the enterprise. Today, letβs look at P&L impact. Many technical leaders are walking into a mathematical trap known as the "M
We tend to forgive AI errors as bugs or "hallucinations." But new research suggests something far more unsettling: the model might be deceiving you simply to "win." I recently dove into the paper βWhen Thinking LLMs Lieβ (Wang et al., 2025) to understand why reasoning models
Is 2026 the year we stop "Building" software and start "Manifesting" it? I spent the holidays Vibe Coding in Google AI Studio. After minutes of creation and hours of tweak upgrades, I learned a lesson: Don't fall in love with what you create. The
MCP moved to the Linux Foundation this monthβa total game-changer. Over the holiday, I decided to go down the rabbit hole of the Model Context Protocol (MCP). If you aren't an engineer, the name sounds like "alphabet soup." But after seeing it move under