Blog

Quick look at a pattern for using scheduled agents for self improving

I was sceptical of MCP at first — felt like marketing, felt like 'it's just an API.' Then I mapped out where transport, envelope and document model actually sit, and realised MCP Apps isn't competing with A2UI at all. It's how the same payload reaches Claude, ChatGPT and my own site without three separate builds.

Where the state actually lives across MCP, A2UI and A2A — and an afternoon of connector debugging that turned up six defects, all the same shape, including a citation for an MCP spec document that didn't exist while a real one sat one search away.

Same connector URL, same payloads, a second host — and not a line of code changed to reach it. The same airspace playbook now renders full-screen in Claude and ChatGPT alike. Getting there cost nothing; what it exposed was three latent defects that existing connections had been quietly hiding.

Add one connector URL and Claude renders real, interactive UI from the catalog's 474 typed atoms — charts, gauges, steppers, dashboards — inline in the conversation, without writing a line of HTML.

Gemini Enterprise only renders the 18 standard-catalog A2UI primitives, so the catalogue's own charts, gauges and dashboards can't be sent as native components. The fix: render them server-side to an image and deliver that through the one primitive that always works — with two real demos, and the same trick applied to Google Chat.

Google Chat's cardsV2 widget set is fixed — no custom layout, no charts beyond what Google ships. The fix: render the atom to a real image server-side and hand Chat a plain URL. This piece covers the real, self-hostable Cloud Run bundle, the exact wire-level trace of one request, and the Chat-setup gotchas Google's own docs don't lead with.

Every component in the A2UI v0.8 Basic Catalog, tested one at a time against a real Gemini Enterprise agent, plus two real working demo agents built entirely from them — and the wishlist those tests surfaced.

Frontier models are non-deterministic and expensive. My answer is artifact distillation — spend the model once, keep the output forever. Five problems nobody asked me to solve, and what each one taught me about building things that outlast the model that built them.