AI Agent Mastery • Module 17
Cognitive Feedback Loops
Systems that learn from experience
Module 17 of 30
"Dear Marilyn,
Our AI testing tool makes the same mistakes over and over. How do we make it actually learn?
— Groundhog Day"
Marilyn Responds:
Learning requires feedback. If your AI doesn't know when it's wrong, it can't improve.
Cognitive feedback loops connect outcomes back to decisions. When a generated test finds a bug, the system learns what made it effective. When it produces a false positive, it learns what to avoid.
Over time, the system gets smarter—but only if the loop is closed.
The Learning Loop
Effective feedback loops include:
- Outcome Tracking — Recording what happened after each decision
- Attribution — Connecting outcomes to specific choices
- Model Updates — Adjusting behavior based on feedback
- Validation — Ensuring changes actually improve performance
Quick Check: Module 17
Question: What's essential for AI systems to learn from experience?
a) More training data
b) Closed feedback loops connecting outcomes to decisions
c) Faster processors
d) More complex models
(Answer: b — Without feedback, AI can't distinguish good decisions from bad ones.)