A career across the full breadth of IT.

Building pricing software: “Make it flexible enough for every pricing model and data structure - but simple enough to use without a manual”.

Configurable software creates an interesting paradox.

It is usually harder for vendors to build, test and maintain. Every additional rule, dependency and exception creates more possible combinations - and more opportunities for something to break.

But pricing is rarely simple

Different markets, customer segments, contracts, currencies and approval levels all introduce legitimate complexity.

So, what happens when pricing software is not configurable enough?

The complexity does not disappear. It moves somewhere else:

• Pricing rules and up in spreadsheets
• Exceptions are managed through email and Slack
• Every change becomes an engineering request
• Critical knowledge lives in the heads of a few people

The goal should no be to eliminate complexity. It should be to put it in a system twhere it can be tested, governed and understood.

Configurable software is technically harder to build, but usually better for pricing teams.

Because if your pricing platform cannot represent how your business actually prices … is it simplifying the work, or simply hiding it?

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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/

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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

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