Key Issues in the Engineering Implementation of AI Agents from the Perspective of Harness Engineering

Harness Engineering focuses on how to constrain, observe, and validate AI Agents in real software engineering environments, rather than relying on longer prompts or switching models. LangChain, using GPT-5.2-Codex and only adjusting the System Prompt, Tools, and Middleware, raised the Terminal-Bench 2.0 score from 52.8% to 66.5%. MiniMax's OctoCodingBench shows that Claude 4.5 Opus has a CSR of 91.2% and an ISR of only 36.2%, indicating that a high compliance rate on individual rules does not mean that a single task satisfies all requirements.