The strategic shift from vibe coding to agentic engineering for production AI

Q: What is the difference between agentic engineering and vibe coding for financial services teams?

Vibe coding produces AI-generated prototypes quickly but without the specification documents, behavioral test records, or audit trails that regulated environments require. Agentic engineering has a structured eight-step cycle for code generation, producing systems with traceable decisions and deterministic test suites. For financial services teams operating under SR 11-7 and SR 26-2, the test suite written before development is also the validation evidence regulators expect.

Q: How should enterprises govern agentic AI?

Trusting AI agents with production data requires reproducible environments, audit trails, and data access controls. A platform that embeds these by default ensures every agentic workflow is governed from day one, rather than retrofitted for compliance after deployment. Teams that skip this foundation risk repeating the operational failures that ended the first wave of traditional ML model deployment.

Now that AI handles code generation almost instantly, the human value in the software development lifecycle shifts into specification and validation.

Nick Jablonski

Field CTO

Domino Data Lab

What you'll take away from this session

Spec first, then prompt

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.

Code generation happens midway through the workflow

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 makes autonomous iteration reliable

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.

Cross-model review catches single-model blind spots

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.

FAQ

What is the difference between agentic engineering and vibe coding for financial services teams?

How does the Ralph Loop work in an agentic engineering workflow?

How should enterprises govern agentic AI?

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