Shortly after starting work in a new role, I was assigned responsibility for a data report that my predecessor, “Charlie”, had previously maintained.

The report was scheduled to run on the first day of each month and had been used for years, seemingly without a hitch.

However, on examining the code behind the report to figure out how it worked, I was shocked to discover a small calculation error that meant every report run to that point had actually been wrong.

Not obviously wrong. The numbers were close enough to correct that they looked completely plausible. But for years, decisions had been made on the basis of figures that were slightly off - without anyone thinking to question them once.

When I raised it, there wasn’t much that could be done. Charlie had already left the organisation. I told the end-users, corrected the report, and kept going.

But the experience haunted me in the years that followed.

My greatest fear as a data scientist has never been the loud errors - the ones that crash a piece of software as soon as you press “run”. It’s the quiet mistakes that scare me, where the program still runs to completion, but produces results that are subtly wrong.

AI is now writing much of the code that Charlie previously would have written. But it doesn’t mean the problem has gone away. In fact, AI has made it even worse.

In the agentic AI era, this problem is called “silent correctness”. And it’s what happens when an agent uses facts that are true to draw conclusions that are false, producing outputs that look perfectly fine - right up to the moment a decision goes very wrong.

In this Value Boost episode of Value Driven Data Science, Jia Huang, lead research engineer at A*STAR and author of Designing AI Agents, joins me to explore why silent correctness is the most dangerous failure mode in agentic AI systems and what data scientists can do to catch it before it causes serious harm.

In just 13 minutes, you’ll discover:

1. Why silent correctness is harder to catch than a hallucination [04:17]
2. Why sampling and auditing are non-negotiable in agentic systems [07:09]
3. Four techniques data scientists can use to catch silent failures [09:28]
4. The one safeguard every agentic AI system should have [11:10]

Listen now on Apple Podcasts or Spotify, or click the link below:

​Episode 120: The AI Silent Correctness Problem​

Talk again soon,

Dr Genevieve Hayes