It’s Time to Redesign How Product Teams Work

Three months ago, I started using Claude Code to handle the repetitive, day-to-day work of product management.

It was an immediate improvement over standard chatbots I have been using for the past few years for handling core PM tasks:

• Deep market, competitive and technical investigations  
• Turning raw customer input into structured change requests  
• Generating high-fidelity specs for the SDLC and user docs

I soon moved from simple tasks to rapid prototyping.

Having coded early in my career (if you know MFC, you know how long ago), it felt natural to create interactive mockups for feature ideas. When ChatGPT came out, I prompted it for Python and JavaScript, copied the code into an IDE, and fed back errors for fixes. This time, it was a different experience: I could get a working prototype in hours and initial feedback within a day.

A dream of autonomous coding, directly applied to my product

Prototyping worked well, but it made me wonder: Could a coding agent take my specifications and produce full, working features?

Answering this would mean a major shift in my workflow.

I would stop generating piles of specifications for the product team to convert into working features and start managing “executable intent” for agentic coding.  My job was no longer overseeing a list of tasks; it was to architect the specific goals, guardrails, and project context an AI needs to turn a requirement into a production-ready feature.

My ultimate vision was autonomous coding applied directly to my product. The transition would start with small, scoped increments and bug fixes to learn the agentic system’s nuances and build trust. Once we prove success on isolated components, we can scale to larger cross-domain features and multi-agent setups.

So I went searching for design patterns that promised HOOTL (human out of the loop, no ongoing human intervention), and soon the name Ralph Wiggum came up (see the side note).

What is the Ralph Wiggium Loop?

Named after the lovably persistent Simpsons character, the Ralph Wiggum Loop is an orchestration pattern for autonomous AI agents.  Popularized by developer Geoffrey Huntley in mid-2025, it shifted AI from “reactive chat” to “autonomous worker.”

Unlike typical one-off prompts, the Ralph Loop runs an agent repeatedly (like a “while loop”) until a task, such as a complex software refactor, is fully complete. It’s become popular because it solves “context rot”: each iteration starts fresh, so the AI doesn’t get tripped up by its previous errors and can keep working for hours without human oversight.

Ralph showed me how, but being naturally skeptical, I wanted to test it first – a “sandbox” to see if the workflow actually worked in a real project before rolling out full autonomous coding in production.

AI built 90% of a feature, I finished the rest

I picked an open-source framework called NiceGUI (a Python framework for building web UIs) as my playground. 

The idea was to implement a new component, an enhanced data table with inline editing, inheriting from the existing table. It had to be a repeatable process enabled by the agentic coding loop, so I decided to build one.

First, I used Claude Code to scout the repository and extract “institutional knowledge” – the specific tech stack, coding patterns, and standards of the project. I fed this context and a detailed feature request into a custom Ralph PRD Factory. This turned my requirements into a JSON-formatted PRD file that the Ralph loop is driven by and that serves as the “executable intent.”

After one day of prep work, I turned the agent loose. In under four hours, it completed the task autonomously – or so it told me.

In reality, I spent a few more hours weeding out UI bugs and minor quirks until it finally felt complete. Still, having the agent take me to 90% was a clear success. The autonomous coding worked.

Redesigning how our product team works

In traditional development, ideas often get lost in translation, passing from PM to designer to engineer to QA, each handoff can dilute or distort the original intent.

When AI builds features from scratch, the old handoff-heavy workflow falls apart. To succeed, teams need to shift from doing manual work to orchestrating the system:

• The PM as Intent Architect – The PM’s output is no longer a ticket; it is the “brain” of the feature. By owning the context engine, the PM ensures the agent has the exact data and guardrails needed to execute. Success is measured by the precision of the context, not the volume of tasks.
• The Engineer as Architectural Oversight – Senior developers can stop spending 80% of their time on boilerplate. They become guardians of the system, focusing on high-level architecture, security, and complex logic that agents cannot yet handle.
• The Designer as Real-Time Reviewer – Instead of static handovers, designers review live agent output as it is generated. They move from static creation to dynamic orchestration, adjusting the visual intent on the fly.
• The QA as Quality Architect – Testing moves from manual bug-hunting to designing “self-healing loops.” They build automated systems to catch failures early, allowing humans to focus on strategy and edge cases.

Let the AI orchestration games begin

This experiment wasn’t just about building a feature in hours, it offered a glimpse into a bigger shift in how we create products. Autonomous agents building features are becoming reality. It’s not a magic solution, we still have a lot to learn about using agentic AI effectively and guiding it toward the results we want.

This shift doesn’t eliminate specialized roles, it moves us away from manual “doing” and toward orchestrating and designing systems.

To succeed, we all need to level up and expand our roles – moving from doing manual tasks to guiding and orchestrating AI. I’m excited about this shift. It’s time to stop just managing backlogs and start shaping the future.