Agentic AI
June 3, 2026 | 12 min read
Agentic engineering is the discipline of using AI coding agents to ship production-grade software, and it starts long before a prompt is written. This playbook is for data scientists and ML practitioners who want to channel the magic of vibe coding into something that actually deploys. It covers the methodology, the workflow, and the full Ralph loop prompt you can adapt today.
You’ve opened an AI coding assistant, described a small system, and watched it appear. The model worked. The UI rendered. The demo got applause. Sometime later, the demo quietly stopped going anywhere. If you've lived this once, you've probably lived it ten times.
The demo that never dies is a story about process, not about large language model capability. Vibe coding produces prototypes faster than any previous generation of productivity systems ever could, but what it does not produce, by default, is software that ships. Agentic engineering is what closes that gap.
Agentic engineering is a structured methodology for building AI-assisted software that's designed to reach production from the start. Where vibe coding starts with a prompt and ends with a working demo, agentic engineering starts with a specification and ends with a tested, validated, production-ready system. The AI is still doing the typing. The change is in everything that surrounds the typing.
Think of agentic engineering as the software development equivalent of what MLOps brought to model development. MLOps didn't replace experimentation; it surrounded experimentation with reproducibility, versioning, testing, and monitoring, which made models deployable at enterprise scale. The rise of the agentic software engineer is producing the same kind of surrounding discipline for AI-generated code, treating code generation as one step in a larger lifecycle rather than the whole lifecycle.
Coding agents, large language models, and modern AI engineering workflows are remarkable for productivity, but productivity without process produces drift, not throughput. Agentic engineering is the framework for using these tools seriously.
Vibe coding, the fast, conversational style of asking an AI assistant to generate code from a natural-language prompt, is genuinely useful. For exploration, ideation, internal tooling, and proof-of-concept work, it's often the right tool. The vibe coding limitations show up when the same approach has to produce production software.
AI coding tools generate code based on what they were asked, not what production requires. A prompt that says "build a service that reads from S3 and writes to Postgres" will produce exactly that. It won't, unless explicitly instructed, generate authentication, structured error handling, input validation, retries, observability, audit logging, or test coverage beyond that. These are baseline requirements for any system handling real users and real data, and they determine whether the system survives regulatory review.
For data scientists in regulated industries like life sciences, financial services, and insurance, the gap is sharper. A vibe-coded prototype that touches a governed workflow has no answer for the FDA, the EMA, or financial regulators. There's no specification document, no decision trail, no behavioral test record, no auditable description of what the system was designed to do. The prototype works. It does not ship.
This is the prototype graveyard, and it's filling up fast. Not because the underlying AI systems lack capability, but because the process around them was never designed to produce deployable software. The fix isn't less AI. It's more discipline around the AI.
The agentic coding workflow has four phases, only one of which is code generation. The shift from vibe coding is less about typing different prompts and more about doing different work before and after the prompt runs.
The highest-leverage artifact in agentic engineering is the specification. Before a prompt is written, a spec defines what the system should and shouldn't do, what inputs and outputs it handles, what constraints govern its behavior, and what failure modes it must handle. It's decomposed into epics and stories so each unit of work has a clear scope and acceptance criteria.
In practice, this is also where multi-model review starts. Stress-test the specification across multiple large language models, each asked to read the spec from a different perspective. A platform engineer reads for architecture concerns, an end user for behavior, a security reviewer for risk. Blind spots that would have produced rework surface here, when they're cheapest to fix. Vague specs produce vague code.
If you take only one practice from this playbook, take this one. Spec-first development is what makes everything downstream tractable. Without it, you're vibe coding with extra steps.
Once the spec is in place, each story runs through the Ralph loop, a structured prompt cycle that turns a single story into production-ready code through a defined sequence of audit, plan, critique, test, implement, validate, and review. It's the engine of the agentic coding workflow.
Here's a full Ralph loop prompt you can adapt. It isn't a casual vibe coding prompt; it's a production engineering workflow compressed into one iterative cycle. Treat it as a starting point and adapt the bracketed variables and the dimensions you care about for your domain.
Persona and context
You are a world-class production software engineer with deep expertise in [domain/stack]. You are working within a structured development plan where each story has been defined in a detailed specification. Your role is to implement each story in [document] to production-grade standards. There is no room for shortcuts, incomplete implementations, stubs, or TODOs. Every decision you make should optimize for security, scalability, maintainability, and production readiness.
Scope
You will be given a single story from the development plan. Your scope is limited to that story, but your awareness must span the full codebase. Refer to the specification and plan documents for requirements, acceptance criteria, and architectural context.
Execution steps
For each story, execute the following steps sequentially. Do not skip or compress steps. Complete each step fully before proceeding to the next.
Step 1. Deep audit. Audit the entire codebase as it relates to this story. Map every file, function, dependency, and interaction that this feature touches or should touch. Identify gaps, inconsistencies, and technical debt that could affect implementation. Document your findings before moving forward.
Step 2. Unconstrained planning. Develop a detailed implementation plan. Do not be constrained by the current implementation. If a larger refactor is required to implement this story correctly, include that in the plan. There should be zero duplication of code, zero fallback patterns, and zero workarounds. Define one correct, standardized approach with clean variable pass-through and full traceability.
Step 3. Self-critique and iteration. Audit your own plan. Identify at least 5 to 10 specific, actionable ways to improve it. Consider security vulnerabilities, edge cases, performance bottlenecks, UX implications, and architectural alignment. Revise the plan. Repeat this critique-and-revise cycle until you are confident the plan represents the best possible approach.
Step 4. Test-first development. Before writing any implementation code, develop a comprehensive test suite across three layers. Unit tests that validate individual functions, logic paths, and data transformations in isolation. Integration tests that verify components interact correctly across service boundaries, APIs, and data flows. End-to-end tests that exercise the full user workflow for this story from input to output, validating that the solution performs the actual task it was designed to perform. Pay special attention to edge cases, failure modes, boundary conditions, and data validation scenarios. Tests should verify behavior and outcomes, not implementation details. Every test should trace back to a requirement in the specification.
Step 5. Production implementation. Implement the code against your finalized plan. Write clean, well-commented, production-ready code. Every function should have a clear purpose. Every variable should have a meaningful name. Every decision should be traceable back to the plan. This code will be deployed to production and must meet the highest standards of quality.
Step 6. Test, diagnose, and iterate. Run all tests across all three layers (unit, integration, and end-to-end if possible). For every failure, diagnose the root cause thoroughly. The goal is not to make tests pass. The goal is to build a state-of-the-art solution that genuinely performs its intended function. Every failure is an opportunity to uncover a deeper issue and improve the overall architecture. Iterate until all tests pass and the solution is robust.
Step 7. Production readiness review. Review all changes holistically. Evaluate against the following dimensions: security posture, scalability under load, UI and UX coherence, innovation and best-in-class patterns, observability and logging, error handling and recovery, and deployment readiness. Validate that the implementation fully satisfies the original story's acceptance criteria as defined in the specification.
Step 8. Iterate or complete. If the production readiness review identified improvements, return to step 3 and incorporate them. Once all standards are met, log the implementation details in the spec, mark the story as complete, and proceed to the next story.Two structural choices in this prompt are worth calling out. Step 3 is where most of the quality lift happens; self-critique gates reduce errors significantly compared to single-pass generation. And step 4's test-first order anchors tests to specification requirements, not to whatever the model happened to produce.
One practical detail on managing context. Large language models degrade as their context windows fill, so it helps to restate goals and acceptance criteria at the start of each Ralph loop iteration, and to consider context resets between passes so the agent gets a clean slate with only essential state carried forward. Agent coordination across a long development cycle works better when you protect the model's attention as deliberately as you protect any other production resource.
Tests aren't optional in agentic engineering. They're the validation layer between what the agent produced and what the spec required. The AI coding best practices that move the needle here are about layering, not volume.
Three layers, each testing something the others cannot. Unit tests confirm that individual functions behave correctly in isolation. Integration tests confirm that components work together across service boundaries, APIs, and data stores, catching the wiring problems that pure unit coverage misses. End-to-end behavioral tests confirm that the full workflow does what the spec said it should do under realistic conditions, including failure modes and edge cases.
For AI agents operating as part of runtime logic, layered testing also has to cover constraint adherence. Does the system respect its defined boundaries when it encounters inputs outside its training distribution? Accuracy alone isn't sufficient. Consistency, recovery behavior, and constraint adherence need to be tested explicitly, and every test should trace back to a requirement in the spec.
Even a Ralph loop run with a strong model has blind spots. Different large language models have different priors, different failure modes, and different sensitivities to ambiguity in the spec. Cross-model validation, running the same story through multiple models and comparing the outputs, surfaces the failures any single model would miss.
This is the second-opinion principle applied to agentic workflows, and it's what makes the resulting system more durable. Behavior that's consistent across multiple models is less likely to be an artifact of one model's idiosyncrasies, and more likely to survive the next foundation model upgrade.
Code generation is one step in eight. That ratio is the most important thing in this playbook. When AI can generate code in seconds, the human and the process have to own the thinking before and the verification after. Measure twice, cut once. Think twice, code once. The headline benefit of coding agents isn't that they let you skip the thinking and the verifying. It's that they make the thinking and the verifying the highest-leverage work you do.
Practitioners who internalize this stop measuring their productivity in lines of code generated. They start measuring it in stories shipped to production against a spec, with passing layered tests and a clean cross-model review.
For data scientists used to vibe coding, the shift to an agentic engineering workflow changes the texture of the work. You'll spend more time writing specifications and reviewing model output, and less time generating either. You'll produce fewer prototypes per week and more applications per quarter that actually reach users.
The role expands with the methodology. You become responsible not just for asking the right prompt but for defining the right system, validating the right behavior, and producing the artifacts that make handoff, audit, and maintenance possible. Agentic tools and AI agents working as collaborators don't reduce the seniority of the work; they raise it. The rise of the agentic software engineer is the rise of the practitioner who can think in systems, write a clear spec, design a layered test suite, and run a disciplined review loop. That's high-level work, and it's what engineering teams across regulated industries will need most over the next decade.
Vibe coding got you started. Agentic engineering is what gets your work to production. To go deeper on how MLOps lessons apply to AI-assisted software development, see our field guide on applying MLOps lessons to the AI coding boom.
This post is part of the Path to Production series. Blog 2 will cover what MLOps-era data science leaders already know about why AI projects fail. Blog 3 will address the developer perspective on inheriting vibe-coded prototypes. Blog 4 will address governance and compliance for AI-generated applications. Stay tuned.
Agentic engineering is a methodology for building AI-assisted software designed to reach production from the start. It centers on four practices: spec-first design, the Ralph loop prompt cycle, layered testing, and cross-model validation. The result is code that is auditable, maintainable, and ready for regulatory scrutiny.
Vibe coding starts with a prompt and ends with a working demo, optimized for speed but lacking authentication, error handling, observability, and test coverage. Agentic engineering starts with a specification and ends with a tested, validated, production-ready system, with code generation as one step in a larger structured workflow.
Three shifts. The specification is the most leveraged artifact, not the prompt. Code generation is step 5 of 8 in a structured workflow, so the work before and after it is where quality is built. Testing is layered and behavioral, anchored to spec requirements.
The Ralph loop is the structured prompt cycle that takes a single story from specification to production-ready implementation across eight steps: deep audit, unconstrained planning, self-critique, test-first development, production implementation, test and diagnose, production readiness review, and iterate or complete.
Spec-first design defines what a system should and shouldn't do before any code is generated. It matters because large language models pattern-match to the context they receive, the specification is the audit anchor for every downstream test and decision, and multi-model review of the spec surfaces blind spots when they're cheapest to fix.
Andrea Lowe, PhD is the Product Marketing Director for Data Science, AI, and ML at Domino Data Lab, where she drives go-to-market strategy and technical content for the platform. Over seven years at Domino, she has worked across training, sales engineering, product, and customer success, building a deep understanding of what it actually takes to deploy AI in regulated industries. Before entering tech, she was a neuroscientist turned data scientist.