Do AI Agents Need Mentors? A Case for CPI

TL;DR. In multi-step reasoning, small errors compound. Chain-Pattern Interrupt (CPI) is a lightweight oversight layer that pauses an agent before a cascade forms, injects a quick self-audit (or external audit), and then resumes execution. Single-agent trials cut cascade-type failures roughly in half; in multi-agent sims, CPI defuses escalations early.

Why “mentors” for agents?

Humans learn safely with mentors who interrupt bad trajectories. Agents don’t. CPI adds a mentor-like pause: detect risky chain patterns → interrupt → run a targeted check → resume corrected.

Implementation sketch

• Wrap each agent/tool step with a CPI guard.
• Keep a rolling chain signature; when risky, call a validator (self or external).
• Proceed or revise based on verdict.

CPI isn’t “extra thinking”; it’s directed interruption—brief, precise, placed where it matters.

Full PDF is linked on the Work page under CPI.