The Mechanical vs. The Semantic: What Happens When AI Memory is Wrong?

We talk a lot about giving AI agents persistent memory—building a "Second Brain" or "Knowledge OS" where agents can log decisions and retrieve context.

But what happens when that memory is wrong?

I’ve been thinking about the gap between mechanical execution (the agent called the tool, the code compiled, the exit code was 0) and semantic truth (the conclusion drawn from that execution is actually correct in reality). It’s easy to assume that if the mechanical layer is solid, the semantic layer will follow. But I started suspecting this might be a dangerous assumption.

To test this, I didn't want to just theorize. I ran a controlled experiment on my own MCP codebase-intelligence server (Python, 50K LOC), which features an IntelligenceStore — a persistent memory layer where agents can log incidents and collect Architectural Decision Records (ADRs).

I wanted to know: If an agent's memory is poisoned with a mix of true and false facts, does it verify against the code, or does it blindly trust its memory?