How have software engineering fundamentals changed with agentic coding? Even when you use a coding agent to write all your code, understanding software fundamentals is important for steering your agent to make the tradeoffs you want — or to even know what tradeoffs exist to be made. Additionally, when you’re building an AI application, the AI core is often expressed through a broader software application, which you will want to help build or shape.
A novice who vibe codes without understanding software fundamentals can create simple applications, but this often leads to the coding agent making bad tradeoffs in latency, availability, consistency, reliability, maintainability, simplicity, and/or cost. In such cases, the developer didn’t know such tradeoffs even existed and therefore did not steer the agent to make the right decisions for their application context.
This article describes what our study of AI Engineering Skills shows are the most important things to know in software engineering. It requires being skilled at:
Building full-stack applications
Managing data
Designing system architectures
Making systems secure and reliable
Scaling and operating in production
Building full-stack applications. Agentic coding enables many developers who previously played more specialized roles (like front-end developer or mobile developer) to play a broader, full-stack role. A coding agent can help with parts of the development process that you might be less familiar with. However, understanding how the full stack actually works is important. Skilled developers understand the key components and concepts of front-end and back-end systems, including UI components, caching, page rendering, API choice and design, authentication, state and session management, asynchronous processing, data persistence, testing, security, and accessibility.
Managing data. Data deserves special attention because it is a foundation that software is built on top of, that is relatively hard to change (even if agents help with migrations). When you know how to manage data, you can think through access patterns and use them to decide what to store and for how long. You can identify the right data models and select the appropriate storage types (such as relational tables, documents, key-value, or graphs) and infrastructure, which in turn affects speed, scalability, availability, reliability, and cost. You understand transactions, concurrency, and how to ensure your data is clean, consistent, and fresh. When needed, you can ensure proper privacy, governance, and compliance. You know how to manage the data lifecycle.