When Code Becomes an Intermediary: Rethinking How AI Produces Software

The emergence of competent coding agents is prompting a strategic rethink of how software ought to be produced and maintained. Historically, naming, modular design, and readable source code evolved as conventions to make programs intelligible and manageable by people; those conventions may be unnecessary if autonomous systems can generate, validate, and maintain behavior directly. Practical demonstrations at industry events and meetups have shown agents not only writing code but orchestrating tests, interacting with live browsers, fixing front-end bugs, and iterating on results with limited human prompting. These agentic feedback loops — act, observe, adjust — combined with expanding model context windows, materially reduce the back-and-forth that once dominated development cycles. The commercial market is already signaling appetite: teams report rapid adoption of such tools, and some vendor offerings have achieved substantial annualized run rates soon after launch. That traction matters because real-world usage surfaces integration, observability, and operational challenges that pure lab demos do not. If models can both produce and self-verify behavior, the industry may shift toward artifacts that capture intent, audit trails, and behavioral contracts rather than relying solely on human-readable source files. This would change testing practices too: rather than hand-authoring unit tests for human reassurance, agents might simulate and prove outcomes internally or depend on formal specification languages. Engineers would not disappear but would migrate toward defining precise intents, crafting specifications, curating model behavior, and building governance around AI-driven pipelines. The transition raises thorny questions about provenance, explainability, rollback, and vendor lock-in: ephemeral or checkpoint-encoded outputs complicate traditional versioning and incident response. Regulatory and security considerations will shape which approaches gain traction, and firms that treat code as the only source of truth will need to adapt policies and toolchains. Ultimately, whether we optimize for human-readable code or the clearest path from goal to reliable behavior will determine job roles, tooling investments, and how trust is established between organizations and the systems they rely on.