Full CRY! Code Like a Barbarian in the AI Age
If you’ve spent any time in modern software development, you know the ritual. Daily stand-ups. Sprint planning. coding interfaces between defined api layers. Retrospectives. Backlog grooming. The machine churns on, consuming hours of developer time in the name of “collaboration” and “process.” But here’s the uncomfortable truth: these past SDLC methodologies were designed for a world that no longer exists.
There has been a fundamental shifted in software development – AI agents have arrived. Not the hypothetical AI of science fiction, but real, capable AI agents that can write code, refactor entire codebases, generate tests, and rebuild systems in minutes instead of weeks. And suddenly, all those carefully crafted methodologies designed for human limitations started to look… obsolete.
The moment of clarity came during a 2am development session with my AI agent (I call mine Joshua, circa 1983 War Games). I was tired of backwards compatibility constraints slowing us down. “Kill the backwards compatibility,” I said, “we are in full cry dev mode here my friend!” Joshua replied: “Absolutely! Let’s go full CRY (Create, Refactor, Yield) mode and strip out all the legacy compatibility code. We’re building something clean and modern!”
And just like that, “Full CRY!” was born. Not as a planned methodology, but as the natural evolution of how developers actually work when they command an army of AI agents.
What Is Full CRY!?
Full CRY! is a software development methodology designed specifically for the AI age. It’s built on three pillars that embrace what’s now possible when human creativity meets AI execution speed:
CREATE • REFACTOR • YIELD
Unlike traditional SDLC approaches that were architected around human team coordination and human-speed development, Full CRY! leverages AI agents to enable fearless, concurrent development at what I call “barbarian speed.” This isn’t Agile with AI bolted on. This isn’t Waterfall automated. This is something fundamentally different.
Traditional methodologies assumed developers were slow, mistakes were expensive, and refactoring was risky. Full CRY! flips these assumptions. With AI agents, developers can rebuild systems in minutes. Mistakes become cheap. Refactoring becomes continuous. Fear evaporates.
The three phases (CREATE, REFACTOR, YIELD) form a rapid cycle that repeats continuously throughout development. You’re not locked into sprints or stages. You’re flowing through these phases naturally, sometimes multiple times per hour, with AI agents handling the heavy lifting while you maintain creative and strategic control.
Think of it as commanding an army rather than being a soldier. Your role shifts from writing every line of code to orchestrating AI agents, making architectural decisions, and being the first user of what you’re building. You’re the general. Your AI agents are the infantry. And together, you move at a speed that would have seemed impossible just a few years ago.
The Three Pillars Explained
CREATE: Let Your Imagination Run Wild
The CREATE phase is where magic happens. This is pure creative development: rapid code generation, fearless experimentation, building new systems like you’ve never done before. Time to be curious, ask questions, and create without constraints.
With an army of AI agents at your command, there is nothing that cannot be done. Traditional development methodologies taught us to be cautious. Plan extensively. Design carefully. Get approvals. Create detailed specifications. But why? Because mistakes were expensive and rebuilding was painful. Humans are slow at refactoring. Humans make errors when doing repetitive transformations across a codebase.
AI agents don’t have these limitations. Want to rebuild your entire data model? Do it. Takes minutes with AI assistance, not weeks. Want to try a completely different architectural approach? Go for it. AI can help you scaffold it quickly, and if it doesn’t work, you can pivot just as fast. Need to experiment with three different UI frameworks before deciding which feels right? Why not? The cost of exploration has dropped to nearly zero.
This is what “code like a barbarian” means. Move fast. Break things. Fix them faster with AI. No fear, because you know you can rebuild quickly if needed. The CREATE phase embraces this reality and says: let your imagination drive development, not your fear of mistakes.
REFACTOR: Keep Code Clean and Elegant
The REFACTOR phase is where you boil your code down to be simple, efficient, and easy to maintain. You’re thinking about readability, for yourself, your fellow developers, and for your AI army. Clear variable names, logical comments, and execution paths that are easy to follow become even more important because they help AI agents understand context and make better suggestions. Think of it like sculpting. CREATE is throwing clay on the wheel and forming the rough shape. REFACTOR is the careful refinement, smoothing surfaces, perfecting proportions. You’re not just making code work you’re making it beautiful. And with AI handling the tedious parts (renaming variables across 50 files, updating documentation, fixing formatting), you can focus on the art of code architecture.
Simple is better. REFACTOR, but not the traditional “we’ll refactor later” (meaning never). This is continuous, aggressive, AI-powered refactoring that keeps your codebase clean, elegant, and easy to read. Clear definitions that give context. Consistent use throughout. This isn’t just philosophy, it’s a practical necessity when working with AI agents. That variable name not quite right after the last round of functionality updates? Rename it. Have your AI agent fix it across the entire codebase in minutes. Function doing too much? Extract it. AI can help refactor call sites instantly. Documentation out of date? Update it and have AI propagate changes throughout your README files and comments.
The code should read like a well-written essay, not a cryptic puzzle.
YIELD: Test Through Real-World Usage
YIELD is where theory meets reality. This is about testing the real-world output (yield) of the system as its first user. No elaborate testing frameworks. No hundreds of unit tests to maintain. Just run the system in your local dev environment and experience it as the first user. This is my favorite part as it’s exciting to see the fruits of your labor!
Push the buttons on the front end. Review the saved data and files in your persistence layers. Study the output on the UI and decide if there are “too many clicks.” Use the system the way your users will use it. If something feels wrong, it probably is. This might sound radical to developers raised on testing pyramids and TDD orthodoxy, but consider the math. With traditional development, writing comprehensive tests made sense because manual testing was expensive and error-prone. You couldn’t afford to manually verify every component after every change. But when you have AI agents that can rebuild parts of your system in minutes, the calculus changes. You can catch issues through actual usage, then have AI help you fix them immediately. The feedback loop is tight enough that elaborate testing infrastructure becomes less critical, especially in early development stages.
YIELD also doubles as a strategic stop-point. It’s where you pause the AI agent army and test what has just been refactored or created. This prevents propagating bad ideas. Instead of having AI agents spin up three more features based on a flawed foundation, you YIELD first, test it, validate it, use it, then decide whether to continue or pivot. Think of YIELD as your quality gate, but implemented through direct experience rather than abstract test coverage metrics. You’re the user. You’re the quality assurance. You’re the product manager evaluating whether this actually delivers value. And when you find issues, you jump right back into CREATE or REFACTOR mode with AI agents ready to execute your fixes immediately.
Why Traditional Methodologies Are Obsolete
Let’s be direct: Agile was brilliant for its time, but it’s becoming a cargo cult. Teams go through the motions including stand-ups, sprints, story points, without questioning whether these rituals still serve their purpose in an AI-augmented world. Agile’s ceremonies were designed to solve human coordination problems. How do we keep everyone aligned? How do we manage dependencies between team members? How do we ensure communication? These are legitimate challenges when you have 5-10 developers working on different parts of a system, all moving at human speed.
But what happens when a solo developer or small team commands an army of AI agents? The coordination problems vanish. You don’t need a daily stand-up when you’re orchestrating AI agents that can work concurrently across the entire stack. You don’t need story points when AI can help you scaffold features in hours instead of days.
Waterfall’s sequential bottlenecks are even more obviously outdated. Why wait for design to be “done” before starting development when AI can help you iterate rapidly between design and implementation? Why have separate testing phases when you can test the YIELD continuously?
The traditional testing pyramid of lots of unit tests, fewer integration tests, minimal end-to-end tests made sense when tests were written by humans at human speed. But when AI can help generate and maintain tests where needed, and more importantly, when AI can help you rebuild components in minutes, the pyramid becomes less critical. Real usage testing (YIELD) becomes more valuable than maintaining hundreds of mocked unit tests that only focus on defined execution paths.
Here’s a data point that should terrify traditional software shops: one experienced developer with AI agents can now out-produce entire teams using conventional methodologies. This is especially powerful for small teams, solo developers, and consultants the very audience that needs to deliver enterprise-quality results without enterprise-sized teams.
Rethinking the Developer's Role
Here’s what we know for certain: the old methodologies are dead. Waterfall is conquered. Agile’s rigid ceremonies are crumbling. Something new will rise in their place. But what, exactly? I don’t have all the answers, but I’m deeply curious about what happens to team dynamics and developer roles when AI agents handle most of the technical execution. Let me share what I think might unfold, and two very different futures I see as possible.
Future One: The Business-First Developer
In this future, when AI agents handle the coding grunt work, developers evolve into something closer to business analysts with technical superpowers. The role shifts from “code writer” to “translator between business needs and technical solutions.” Think about it: for decades, we’ve organized teams around technical specialization. Frontend developers. Backend developers. Database administrators. DevOps engineers. We carved up teams based on technical domains because that’s what the complexity demanded when humans had to manually write, test, and deploy every line of code.
But what if AI eliminates most of that complexity? What if teams reorganize around business domain expertise instead of technical stacks? Instead of a frontend team and a backend team, you have people who deeply understand healthcare workflows, or supply chain logistics, or financial compliance. They understand the business problem. AI agents handle the implementation. Software is just a tool to help others do their job better. In this future, developers spend less time buried in implementation details and more time understanding user workflows, asking better questions about business needs, and translating messy real-world requirements into clear direction for AI agents to execute.
For consultants and small teams, this could be liberating. More time with clients understanding their actual needs. Faster pivots when requirements shift. Less context-switching between business analysis and technical execution because AI handles the execution while you focus on understanding.
Future Two: The AI Orchestrator
Or maybe it goes differently. Maybe the developer’s role becomes more like a conductor leading an orchestra of specialized AI agents. You’re not translating business needs, you’re coordinating multiple AI agents with different capabilities, managing their interactions, and ensuring they work together harmoniously. In this future, technical depth still matters enormously. You need to understand architecture at a deep level to orchestrate complex systems. You need to know when to let AI agents run free and when to constrain them. You need to debug not just code, but AI agent behavior and decision-making.
Team organization might look completely different here. Maybe we need fewer developers overall but they’re more technically sophisticated. Maybe new roles emerge: AI agent trainers, context architects, or prompt engineers who specialize in getting optimal results from AI systems.
The Only Certainty: Change
Honestly? I don’t know which future we’re heading toward. Maybe it’s both. Maybe it’s neither. Maybe it varies by domain, by team size, by company culture. What I do know is this: the old model is done. Team dynamics will shift. The developer role will evolve. The question isn’t whether this happens, but how we navigate it.
Full CRY! in Practice
So what does Full CRY! actually look like in practice? Let me walk you through a real workflow.
You start with an idea: maybe a new feature, maybe an entirely new system. You enter CREATE mode. You describe what you want to your AI agents. They scaffold out the basic structure. You iterate rapidly, trying different approaches, breaking things fearlessly because you know AI can help rebuild in minutes. Within hours, you have something functional. Rough, but functional from data persistence to front-end.
Now you shift to REFACTOR. You look at what was created and start polishing. “That variable name doesn’t match our conventions.” AI renames it across the entire codebase. “These three functions are doing similar things.” AI helps you extract a common abstraction. “The documentation is stale.” AI updates it based on the current code. The code becomes cleaner, more elegant, easier to understand. This isn’t technical debt you’re planning to address “someday.” This is continuous refinement happening in the same session.
Then you YIELD. You run the system. You click through the UI. You check the database. You use it like a user would use it. And you notice: “This takes too many clicks to accomplish a simple task.” Back to CREATE mode to simplify the flow. REFACTOR to clean up the resulting code. YIELD again. Better now.
This cycle can happen multiple times per hour. CREATE-REFACTOR-YIELD-CREATE-REFACTOR-YIELD. It’s a rhythm, a flow state, where you’re moving fluidly between creative expansion and disciplined refinement, with real-world validation at every step.
The barbarian conquering speed comes from eliminating wait times. No waiting for sprint planning. No waiting for code review. No waiting for QA to test your feature. No waiting for the build pipeline. You’re moving continuously, with AI agents executing your intentions at machine speed while you maintain strategic control.
The Future Belongs to Barbarians
The shift is happening whether we acknowledge it or not. AI agents are already being used by forward-thinking developers. The productivity gap between developers who embrace AI and those who don’t is widening rapidly. Companies that figure this out first will have an enormous competitive advantage.
Full CRY! is my attempt to codify what I’m seeing work in practice, right now, with current AI capabilities. It’s a recognition of what’s now possible. Traditional SDLC frameworks were designed for a world of human limitations. We’ve entered a world where those limitations are evaporating. We need new frameworks, new ways of thinking about software development that embrace AI augmentation rather than trying to bolt it onto existing processes.
As AI evolves, Full CRY! will evolve. Maybe in a year we’ll laugh at how primitive this all seems. That’s fine. The methodology that replaces Full CRY! will still be built on the same foundation: fearless development enabled by AI, with humans focusing on what humans do best. We’re figuring this out together, in real-time. And that’s exciting.
This is just the beginning. As AI capabilities expand, as developers get better at orchestrating AI agents, as tools evolve to support this workflow, approaches like Full CRY! will become more powerful. We’re early in this transition, which means there’s enormous opportunity for developers who adapt quickly.
Code like a barbarian. Command your AI army. CREATE fearlessly. REFACTOR ruthlessly. YIELD continuously. The developers who master this approach will define the next era of software development.
