Now that AI handles code generation almost instantly, the human value in the software development lifecycle shifts into specification and validation.
The specification and test suite written before an agent starts work are the control mechanism for the entire development process. They define when the agent is done and what it cannot change.
In a structured agentic engineering workflow, code generation sits in the middle of an eight-step cycle. The steps before and after it — audit, planning, testing, and review — produce production-grade output.
The Ralph Loop is a persistent development cycle that resets context, reinjects the original prompt and accumulated results, and tests against a deterministic exit condition. It prevents the error accumulation that degrades quality in long single-session runs.
Feeding a completed pull request to a second model for review surfaces gaps that the original agent is unlikely to catch. It requires no additional infrastructure beyond a second API call.
Financial services organizations spent years learning that research notebooks don't become production models without reproducibility, validation evidence, and documented assumptions. That discipline, codified in SR 11-7 and extended under SR 26-2, produced the platform infrastructure that exists today. Agentic development introduces the same structural risk in a new form.
Nick Jablonski, Field CTO at Domino Data Lab, draws a sharp distinction between agentic engineering and vibe coding that matters for any team shipping AI-generated software in a regulated environment. Vibe coding produces prototypes while agentic engineering, built around the Ralph Loop and an eight-step structured prompt cycle, produces auditable, tested systems. The difference is what surrounds the prompt instead of the AI tool.
Jablonski demos a live market prediction model built on Domino and highlights reproducibility, audit trails, and governed data access. Organizations that convert AI development speed into production value build MLOps infrastructure that solves this governance problem. Read the agentic engineering playbook for the full methodology, including the Ralph Loop prompt.
