Blog
Vibe coding with Claude can lead to fast code and bad abstractions. Learn how the OODA Loop and interview step improve AI-generated code quality. Part of the 2025 AI Coding Workflow Best Practices Series.
Most enterprise DXP failures stem from a single architectural flaw: fragmented codebases. A monorepo enforces integration discipline, reduces costs, and enables AI-driven personalization. Without it, your platform is at risk of collapse. If you’re responsible for delivering a scalable customer platform, here’s why foregoing a monorepo architecture is a strategic liability.
I recently joined a team in a great position - their GraphQL API PoC was green-lit to become the company's future platform and was nearing feature parity with their existing REST-ish API. As more product teams inside the company started building applications on the GraphQL API, the API team had to dedicate time to support, operations, bug fixes, and new features requested by product teams. Time for performance improvements was squeezed out, so I joined the effort to lower the server's response times and reduce resource usage with my fresh eyes and questions about everything.
I’m not a developer, but I built and deployed a headless AI agent in a single day – at zero cost. In this guide, I’ll walk through my approach, the technologies I used, and how I integrated embeddings from our headless CMS for retrieval-augmented generation (RAG). I’ll also cover how I implemented an open-source AI governance tool to ensure accountability and monitoring.
Adopting a headless architecture enables businesses to launch AI-driven experiences faster – without requiring a complete overhaul of existing tech stacks. Industry leaders like Nike and Alaska Airlines use this approach to cut costs and deliver AI-powered customer experiences that set them apart.
AI initiatives often get stuck in the Proof of Concept (POC) stage or fail to deliver tangible business value. The team might "build the solution," but it doesn’t address the real problem.
AI is transforming how products are built and experienced, introducing new tools, processes, and challenges for product managers, but it doesn’t fundamentally alter their core mandate of delivering customer and business value. While PMs must now navigate complexities like non-deterministic experiences and data readiness, their focus remains on understanding users, solving problems, and driving outcomes, not just implementing AI for its own sake.
Enterprises often struggle with rigid, outdated front-end systems that drive up costs and hinder their ability to adapt to market demands. Here's what a modern, AI-ready tech stack looks like and how you can achieve AI readiness incrementally in complex enterprise environments.