AI Agents Don’t Just Add Load. They Change Its Shape

Abstract visualisation of AI agent database load patterns – compression, expansion and recursion replacing predictable human-paced query traffic

AI agents don’t just add database load – they change its shape. This article explains the three new patterns: compression, expansion and recursion.

AI Can Replace Interfaces, Not Systems of Record

AI replacing the interface layer while the database remains the system of record – the commit is where intent becomes enterprise reality

AI can replace interfaces but not systems of record. The database commit is where intent becomes enterprise reality – and that boundary matters more than ever.

The Hidden Cost of Letting AI Agents Query Live Systems

AI agents querying a live enterprise database – illustrating the hidden performance cost of unplanned, machine-generated SQL on production systems

AI agents querying live databases generate unplanned load that breaks enterprise performance assumptions. This article explains the hidden cost for systems of record.

The Cloud Is Built on Uniformity. Databases Are Not

Cloud infrastructure built on uniformity contrasted with enterprise databases that demand architectural specificity and performance predictability

Cloud infrastructure assumes workloads are uniform. Enterprise databases are the exception – and AI is making that tension more visible than ever.

Why We Spent 20 Years Protecting Databases from Analytics (and Why AI Just Broke That Truce)

Twenty years of ETL-era database protection from analytics workloads – and how agentic AI has broken that long-standing architectural truce

For two decades, DBAs protected production databases from analytics workloads. AI agents have ended that truce and the consequences go deeper than most realise.

AI Doesn’t Read Dashboards… and That Changes Everything for Databases

AI agents bypassing dashboards to query databases directly – replacing human interpretation with machine-speed access to systems of record

AI agents bypass dashboards and query databases directly. This article explains what that means for the architecture of enterprise data systems and systems of record.

Databases Were Built for Humans – AI Agents Change the Equation

Enterprise databases built for human-paced interaction now facing agentic AI that removes the assumptions of think time, session boundaries and deliberate intent

Databases were designed for human-paced interaction – sessions, think time and deliberate intent. AI agents remove all three assumptions simultaneously.

Inferencing Is a Database Problem Disguised as an AI Problem

AI inferencing reframed as a database performance problem – latency, concurrency and data access patterns under real-time machine workloads

AI inferencing is a database problem disguised as an AI problem. Real-time model workloads expose latency and concurrency demands that enterprise storage wasn’t designed for.

The Biggest Gap In The Clouds? High Performance RDBMS

A hurricane representing the performance storm facing high-performance RDBMS workloads that cloud infrastructure was not designed to handle

Hyperscalers have solved cloud infrastructure for most workloads. High-performance RDBMS remains the exception – the gap between cloud promises and database reality is still real.