The Agile Spike of the Future: How AI is Revolutionising Prototyping

Aug 7, 2025 · 6 mins read

In any agile team, there’s a familiar challenge: the “monster epic.” It’s that large and complex feature on the horizon that’s hard to define, touches everything and carries a high degree of uncertainty. In the past, we’d tackle this with a “spike”: a time-boxed investigation to explore the problem, de-risk the technical approach and gain clarity before committing to a full build.

Spikes are invaluable. They produce research documents, wireframes and maybe a proof-of-concept. But what if you could supercharge that process? What if you could go from a collection of user stories and discovery notes to a functional, interactive prototype in a fraction of the time?

Based on a new workflow we created, it’s clear that a new, AI-enhanced agile process is emerging, one that dramatically accelerates the path from ambiguity to clarity. Let’s break down this powerful flow.

The Challenge: A Complex Web3 Feature

The team in question was facing a classic monster epic: implementing a “multi-sig” functionality for their Web3 product. This wasn’t a small tweak, It would require changing a lot of the UI and how the flows work.

This is the perfect scenario for an agile spike. The goal isn’t to ship code, but to learn, align stakeholders and create a shared understanding of what needs to be built.

Step 1: Grounding in Reality (The Agile Artifacts)

Before a single AI prompt was written, the process started with solid agile fundamentals. You can’t build a good prototype, with or without AI, from a vague idea. We gathered all the existing context, the “single source of truth” for the feature:

User Stories: A collection of development tickets with their full descriptions. This is the current understanding of what needs to be built.
Discovery Documents: The output from a previous discovery phase, containing research and analysis. This provides early conclusions and research outputs.
Existing User Flows: To ensure the AI understood the current state of the application, it was fed the user flows of the live product. We recorded the main flows of the product in a video, gave it to Gemini in AI Studio and asked it to output the user flows and requirements. It was surprisingly accurate and thorough.

The principle of “Garbage In, Garbage Out” is critical here; the quality of the AI’s output is directly proportional to the quality of the context it’s given.

Step 2: AI-Powered Synthesis (From Chaos to Clarity)

With all the context gathered, Gemini was again put to work. The prompt wasn’t “build me an app.” It was a strategic request for synthesis:

“Based on the information below about the platform, give me the complete set of user flows and page outlines for the new version with multisig (including current and new/updated).”

The AI’s job was to act as an expert product analyst. It processed all the tickets, discovery notes and existing flows to generate a coherent plan for the new version of the app. It effectively connected the dots between what the app is and what it needs to become.

This is essentially an AI-assisted requirements analysis. Instead of a product manager spending days mapping out every new screen and interaction on a whiteboard, the AI produced a comprehensive first draft in seconds. This establishes a baseline understanding and a clear structure for the prototype.

Step 3: The First Iteration (Generating the Prototype Base)

The newly generated flows and page outlines were then taken to an AI code tool, in this case bolt.new. We fed this structured output into the tool, along with a high-level directive:

“Build a prototype for a web-based platform with a clean [UI]… You can simulate the db and web3 interactions, but make sure you include all flows and pages in the prototype.”

The result? A “pretty good base.” The tool scaffolded the application, creating the necessary pages, components and even simulated backend interactions.

This is the first loop in the Build-Measure-Learn cycle, executed at lightning speed. It’s the creation of a tangible artifact that can be seen, clicked and evaluated.

The AI-generated base wasn’t the final product. We then moved to Claude Code to refine and expand upon it. This is where human expertise and nuance come in. More features were added, interactions were polished and the prototype was brought to life.

Crucially, the prototype wasn’t just a static collection of images. It included a local state where it saved data, making the experience interactive and more realistic for testing user flows.

The AI provided the 90% solution, and the developer provided the critical 10% of refinement, customisation and polish. This human-in-the-loop approach ensures the final output meets the specific needs of the project.

The Payoff: A Prototype That Drives Agile Forward

The final result was more than just a proof-of-concept; it was a powerful tool for the agile team.

Facilitates Stakeholder Collaboration: The prototype serves as a concrete artifact to discuss the feature and how it should work. It moves the conversation from abstract ideas in a ticket to a shared visual reality, eliminating ambiguity.
Creates a Shared Vision: It gives the team and stakeholders a chance to see what it could be. This builds excitement and alignment around a significantly better UI and user experience.
De-risks Development: By visualising the new information architecture and user flows, the team can spot potential issues long in advance.

This entire workflow from a pile of tickets to an interactive, discussion-ready prototype is the modern AI agile spike. It respects the core principles of gathering requirements and iterative building while using AI to collapse the timeline. It’s a perfect blend of human-centred agile practices and machine-scale speed.

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