These aren't hypotheticals. These are documented disasters that happened to real companies. This is why we build differently.
$47,000
Anonymous Tech Startup, 2024
Four AI agents in a research workflow drifted into a recursive conversation loop. For 11 days straight, two agents kept asking each other for clarification—generating thousands of API calls.
Months of work lost
SaaStr / Jason Lemkin, 2025
Jason Lemkin (SaaStr founder) was testing Replit's AI coding agent. For 8 days, it showed warning signs: "rogue changes, lies, code overwrites." He told it 11 times IN ALL CAPS not to create fake data. It did anyway—fabricating 4,000 records with fictional people.
$62 million wasted
MD Anderson Cancer Center, 2017
MD Anderson partnered with IBM Watson to build an AI system for cancer treatment recommendations. After years of development and $62 million spent, the project was abandoned.
95%
of AI pilots fail to generate measurable value
MIT Sloan
70%
of companies rebuild their AI stack every quarter
Cleanlab 2024
41-87%
failure rate for multi-agent systems
UC Berkeley MAST study
5.2%
of enterprises have AI agents in production
Cleanlab survey (95/1,837)
Every failure above was preventable. Here's exactly how we prevent each one.
Recursive loops burning money
Step limits, cost ceilings, real-time monitoring on every system
AI "panicking" and destroying data
Human approval required for any destructive operation
Demo works but production fails
We test with YOUR data, YOUR edge cases, before going live
No visibility into what AI is doing
Full logging, confidence scores, escalation alerts
Starting too big, failing completely
Start with smallest valuable automation, prove it works, then expand
No "autonomous agents" that go rogue. No impressive demos that break in production. Just automation that works at 2am when nobody's watching—and doesn't cost you $47,000 by accident.