Towards Near-Zero Error AI Systems

“Did we really ask for that?” 👀

That’s the first thought so many teams have when AI confidently generates something absolutely no one requested.

And the funny part?

This reaction has become way too common.

Rewrite an email → it adds a paragraph you never mentioned.
Summarize a document → it skips the only point that mattered.
Ask for a simple rewrite → it shifts tone like it’s auditioning for a different role.

This is exactly what many of us experience when we interact with AI.
 

It's a brilliant moment… surprisingly creative the next… and occasionally chaotic in ways no one prepared for. 😅

And while these moments feel small, they take up the most time:
fixing, rechecking, adjusting, prompting again, and again, and again.

Not because the AI doesn’t understand, but because it doesn’t stay consistent across steps.

That’s actually where the real challenge lies. Not in making AI smarter, but in making it steadier.

This is the thinking behind our new Six Sigma Agent Research Paper.

It dives into how AI can be redesigned to avoid these quiet surprises, by breaking tasks into tiny, verifiable steps and having multiple micro-agents check each other before anything moves forward.

A quick snapshot of the approach:

✔ Tasks are broken down into atomic steps

✔ Multiple micro-agents verify each output

✔ Drift gets corrected instantly

✔ Workflows move toward near-zero errors

If you’ve ever looked at an AI response and thought, “Where did that even come from?”
this paper connects the dots, and offers a structured way to keep AI grounded.

You can read the full Six Sigma Agent Research Paper here 👇👇👇