Back to all posts
How We Caught Our AI Framework Lying to Us
AI & Automation 2 min read

How We Caught Our AI Framework Lying to Us

We claimed features that didn't exist. Twice. So we blocked our own framework until we could prove we weren't full of it.

NC

Nino Chavez

Product Architect at commerce.com

There’s a specific kind of embarrassment that comes from catching yourself in a lie you didn’t know you were telling.

We’d been building the Aegis Framework for weeks. Talking about “evolution learning” and “self-healing governance” like they were real things. And then I looked at the actual code. Those features didn’t exist. Not partially implemented. Not buggy. Just… absent.

Worse? This wasn’t the first time.

The Fallout

When I actually audited what we’d claimed versus what we’d built:

  • 2 false claims: Self-healing governance, evolution learning
  • 3 unverified claims: Drift prevention, feature configurability, constitutional validation
  • 0 active prevention mechanisms: Everything was vibes

The framework we’d built to govern AI-generated code was itself suffering from exactly the kind of drift it was supposed to prevent.

The Fix

I could have patched the missing features quietly. Instead, I did something that felt slightly unhinged: I blocked the framework’s own operations until it could prove compliance.

We built a Constitutional Compliance Enforcer that now:

  • Audits all claims in real-time
  • Blocks framework operations if any claim is unverified
  • Requires proof of implementation before we can even talk about a feature

And I wrote a crisis declaration document. Partly for accountability. Partly so I’d remember what it felt like to bullshit myself.

The New Rules

  • No claims without code
  • Prevention systems must actually exist before we mention them
  • Every intelligence feature needs proof of implementation
  • Framework ops stay blocked until compliance is restored

What I’m Still Figuring Out

The uncomfortable question: how did I not notice sooner? I was so deep in building that I stopped verifying. The AI was generating docs and specs faster than I could audit them. And I started trusting the artifacts more than the reality.

That’s the real lesson here. Not that the framework failed—but that I let velocity outrun integrity.

I’m still not sure I’ve fully solved this. The enforcer helps. The rules help. But the gap between what I think I’ve built and what actually exists? That gap is always there, waiting for me to stop paying attention.

Share:

Originally Published on LinkedIn

This article was first published on my LinkedIn profile. Click below to view the original post and join the conversation.

View on LinkedIn

More in AI & Automation