Agentic Coding | Architecture Rules and Verification

AI coding agents can produce software faster than humans can review it, making architectural constraints and automated verification more important than expanding manual code review. In a discussion on The Next Commit, software engineer and author Matt Erman argues that agents’ broad knowledge can create incoherent systems unless teams define clear architectural boundaries, dependency rules, and executable “fitness functions” that test structural compliance beyond ordinary unit and integration tests. He recommends treating each feature as a sequence of independently tested and committed phases, allowing regressions to be isolated to the latest increment and converted into durable regression tests. Erman also frames architecture as a responsibility shared by everyone who changes code, with decisions differing primarily in scope, while engineers increasingly focus on defining system intent, constraints, and evidence rather than writing every line themselves. The piece concludes by presenting SonarQube as a proposed independent verification layer that can provide architectural context to agents, enforce quality gates in CI, and detect long-term architectural drift across codebases.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.