AI Harness Engineering Interview Preparation Handbook

"A production AI agent has two halves: the model and the harness. The model is rented by the token. The harness is the engineering you own."

In 2023, the industry conversation centered on prompts. In 2024, it shifted to agents. In 2025, it focused on tools and context. Today, engineering leadership at the frontier has converged on a single foundational reality: the model is no longer the bottleneck—the harness is.

The evidence is empirical and undeniable:

• Terminal-Bench 2.0 reveals massive performance divergence when the exact same model runs inside different agent harnesses.
• Meta-Harness research proves that searching over harness code alone—with model weights completely frozen—dramatically shifts task success rates across complex code editing, mathematics, and tool-use benchmarks.
• Industrial AI laboratories (OpenAI, Anthropic, DeepSeek) report building mission-critical products where humans write zero application code; instead, software engineers build the operational environment, verification sensors, feedback loops, and execution boundaries that allow autonomous models to operate reliably.
• A prompt whisperer tries to make a single conversation succeed. A Harness Engineer builds the resilient, hardened, deterministic systems infrastructure that allows hundreds of thousands of autonomous agent conversations to succeed unattended—under adversarial injection, context window rot, network partitions, silent provider weight updates, and strict budget caps.

Harness Engineering in Practice is the definitive, code-backed systems-engineering manual for designing, hardening, monitoring, and evaluating production agent runtimes. Spanning 482 pages, 11 parts, 29 chapters, and a companion repository (ai-harness-labs) with 29 runnable labs and 145 worked senior interview questions, this work converts agent development from stochastic folklore into verifiable systems engineering.