Vibe coding moves fast. You sketch functions on the fly, lean on auto-completion, push code with minimal setup. It feels like hacking in a REPL—quick experiments, instant swaps, no formal spec. That speed lets teams prototype features overnight, but it also leaves gaps: missing type checks, half-baked error handling, ad-hoc dependencies. When a minor change breaks production, tracing its origin can feel like finding a needle in a haystack. Agentic engineering brings structure back. You define clear contracts for each “agent” (microservice or AI component), then enforce those contracts with unit tests, schema validators and end-to-end checks. Context engineering sits at the core: every prompt, data schema and environment variable lives in a centralized repo. That way, if you retrain a model or swap out an API, you update one source of truth. A CI/CD pipeline runs nightly builds, runs tests against real and synthetic data, and flags drift or performance regressions. Developers become factory managers. They pick the right AI models for each task, containerize them, then stitch them together with orchestrators like Airflow or Prefect. The paper lays out a four-step roadmap: 1. Catalog existing tools and data sources. 2. Formalize interfaces and write tests for each component. 3. Automate deployments with version control and rollback mechanisms. 4. Monitor performance metrics and set up alerting on anomalies. This approach blends the agility of AI-driven coding with the rigor of traditional SDLC, so teams move fast without losing sight of reliability.