Code Quality in 2026 with AI
Posted on: 2026-02-21
I have been working extensively with multiple AI agents coding in parallel. Imagine dozens of agents operating at the speed of ten engineers. The volume of new code reaches thousands of lines every day. The question is inevitable: how do we preserve quality?
The answer may surprise some, but it is no different from how C++ translates into Assembly or binary. The concern is not readability for humans, it is about effectiveness for AI. Just as we trust compilers to produce efficient machine code rather than elegant source code, we must design code for agents, not humans.
The next stage of software development demands rules that coding and validating agents can agree on. Code must remain intelligible to agents, even in enormous code bases. Agents must recognize reusable functions and avoid repetition, because a concise code base is faster to scan, easier to work with, and preserves context when interacting with large language models. This requires engineers to let go of certain long-held habits, insisting on function names, spacing, or strict organization, and instead focus on enabling agents to operate efficiently.
Adopting an AI-first mindset is not easy for those of us who have built software the traditional way, but once embraced, it unlocks a new velocity in development. Rapid iteration inevitably requires testing, both for humans and AI. Thankfully, AI can generate unit and integration tests at remarkable speed. Instilling the discipline for agents to produce and respect tests goes a long way. Equip your agents with rules to prevent regressions, ensure they create new tests, and provide them with ample knowledge.
The role of the modern software engineer is evolving. We are no longer just writing code; we are building the infrastructure for AI to code, validate, and improve itself. Understanding this, and embracing it, distinguishes those who will shape the next era of software development from those who will merely follow it.