AI Harness Engineering · 84 pages
AI Harness Engineering Interview Prep Handbook
An architecture-first guide to explaining how production AI systems stay observable, testable, and controlled.
Contents
- What a harness is
- Observability by design
- Testing non-deterministic systems
- Control layers and guardrails
- Interview walkthroughs
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An AI harness is the layer of infrastructure that surrounds a model so it can be run, observed, tested, and controlled in production. It is the difference between a demo that works once and a system a team can operate with confidence.
Why harnesses matter
Foundation models are non-deterministic and opaque. The harness is where you regain the properties traditional software gives you for free: reproducibility, logging, evaluation, and safe rollback. Interviewers probe this because it separates people who have shipped from people who have only prototyped.
- Observability: trace every prompt, tool call, and token
- Evaluation: score outputs continuously, not just at launch
- Control: guardrails, fallbacks, and kill switches
When asked to design an AI feature, describe the harness first. It signals production maturity faster than any model choice.
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