A career across the full breadth of IT.

The bugs I worry about most are the ones I’ve already learned to work around.

Use a product every day and its oddities become muscle memory: click here first, refresh after that, avoid this sequence entirely. Eventually, I stop seeing friction and start seeing “how the product works”. The user, inconveniently, has not attended the same training.

That is what I’m trying to get better at noticing. Dogfooding is useful, but familiarity can hide exactly what it is meant to reveal.

I need to watch new users more closely, write down the “little tricks,” and resist answering problems with “just do this”.

Which workaround has become so normal in your product that nobody calls it a bug anymore?

Inspired by the article Bug blindness - https://danluu.com/bug-blind/

#CTOThoughts #ProductQuality

Autonomous agents are missing one important analytics feature: the analyst’s raised eyebrow.

A human notices when a price looks stale or a revenue number feels suspicious. An agent can follow the workflow perfectly and act confidently on the wrong value. More intelligence does not repair missing context.

Before expanding autonomy, teams need to make human sanity checks explicit: freshness rules, data contracts, quarantines, agreed metric definitions and traceability. Start with read access, then earn the right to write.

The encouraging part is that this work is practical and incremental. It also improves the systems humans already use.

Which important data check in your organization still exists only inside someone’s head?

Source: Making Your Data Ready for Agentic AI

hashtag#CTOThoughts hashtag#AgenticAI

AI is making code abundant. Good software remains inconveniently scarce.

Generating a change faster does not help much if environment setup, CI, review and verification stay slow. A thirty-minute pipeline can defeat every frontier model you point at it, without displaying the slightest intelligence.

The useful metric is not lines of code or agent-written pull requests. It is cost per accepted change: the full journey from intent through validation to something we are willing to run in production.

Coding agents do not remove engineering constraints. They expose them faster—and move human attention toward judgment, architecture and evidence.

Where is the real bottleneck after code generation?

Source: When code is abundant

#CTOThoughts #AgenticEngineering