As AI becomes increasingly adept at writing code, the real differentiator will be the engineering system: context, tools, task orchestration, feedback mechanisms, and architectural guardrails.
From HTTP APIs, official SDKs, AI development frameworks, low-code platforms to AI programming tool SDKs, this article outlines 5 patterns of AI application development and a subsequent learning roadmap.
This article focuses on one task: breaking down the Harness Agents/Agent architecture into core components, responsibility boundaries, and execution flows, making it easier to quickly regain a holistic understanding in the future.
When AI-generated code logic reveals bugs days later, and you can't immediately spot the issue or even understand your own work, how do you optimize the development process? The approach: first align with the current state, then modify the code, and finally review and document decisions.
