Context Is the New Code — Patrick Debois, Tessl

Context has replaced traditional code as the primary driver of AI-assisted development, necessitating a structured "Context Development Lifecycle" (CDLC) analogous to DevOps. This lifecycle involves generating, testing, distributing, and observing context to ensure reliability in AI coding agents. Rather than relying on manual prompting, developers should treat context as reusable skills and workflows, incorporating automated testing—such as linters, evaluators, and sandboxed execution—to validate performance. Establishing registries for these context packages allows teams to share standardized workflows, while observability tools and feedback loops help identify gaps and improve agent performance over time. By shifting from ad-hoc prompting to an engineered approach, organizations can manage the inherent non-determinism of AI and scale development efforts effectively, treating context as a critical, versionable asset rather than a transient input.