Sagar Vemala

How we went from observability to evaluation: Golden datasets, RAGAS metrics, and LangFuse feedback loops that made agent quality a measurable number.

The Problem​

After we shipped our Ecosystem Agent, the agentic RAG system that unified Docs, Academy, Storybook, and Marketplace behind a single Ask-AI, the roadmap did what roadmaps do. Better re-ranking. New indexing strategies. Model upgrades. Prompt refinements.

Every enhancement raised the same question: did this change make the system better, or did it quietly break something?

For a while, we answered it manually, running a fixed set of real developer questions against the system and reviewing every answer before release. It worked, barely. And it made one thing obvious: manual verification of intelligence does not scale. It was becoming the bottleneck on our own velocity.

Shipping an agentic system without evals is a blind game. The team's response was the same as last time. Don't add more process. Build the missing layer.

The shift happened. Most projects didn't come along.​

With WaveMaker AI, the standard for building web and mobile applications is now the Design System project — token-driven, component-governed, theme-controlled from the foundation. You define design tokens once, and every component in your application inherits them. Consistency is guaranteed by architecture, not discipline.

The layout model changed too. Design System projects use auto layout to compose pages — replacing the older, rigid grid model with something cleaner, more flexible, and far easier to maintain over time.

The result is better consistency, easier theming, simpler maintenance, and a more future-ready architecture.

How we built a production-grade agentic AI system that unified documentation, learning, components, and marketplace discovery into a single developer experience — and what we learned shipping it.

The Problem​

As our product ecosystem expanded with developer-centric documentation, an Academy, a Storybook of component library, a Marketplace — our knowledge surface grew faster than any developer could navigate.

Finding the right component spec meant visiting Storybook. The tutorial video explaining it lived in the Academy. Checking whether a connector already existed required a separate trip to the Marketplace. Then back to Docs to understand concepts. Four tools. Four context switches. One Product.

Developers were context-switching across four separate surfaces for finding relavant knowledge belonging to one product. There was no unified intelligence layer — fragmented knowledge spread across Docs, Academy, Storybook, and Marketplace, with no thread connecting them.

The team's response was architectural- don't build a better search bar. Build an intelligence layer that understands all four systems and can reason across them.