Performance for agent UIs

The whole View is fetched as one HTML string, mounted in a fresh iframe, and frequently discarded. Every millisecond is on the user's critical path between asking a question and seeing an answer.

Bundle size

The View is inlined into the resource. A 2 MB bundle is 2 MB on every mount.

Bundle to a single file (vite-plugin-singlefile, or esbuild to one ESM module inlined in a <script type="module">). This also empties resourceDomains, which is a security win.
Keep the dependency set lean. A charting library, a date library, and a component kit will each out-weigh your actual widget.
Prefer the platform: Intl over a date library, CSS grid over a layout library, inline SVG over an icon package.
Tree-shake and target modern output — hosts run current engines.
If you must load remotely, declare exactly those origins in resourceDomains and accept the extra round trip.

Rough budget: under 200 KB for an inline card is comfortable, under 500 KB for fullscreen. Past a megabyte, users see a visible gap.

Perceived latency

The bridge hands you the tool arguments before the result. Use them.

app.ontoolinput = ({ city, days }) => renderSkeleton({ city, dayCount: days });
app.ontoolresult = (result) => render(result.structuredContent);

A skeleton built from real arguments — the right city name, the right number of rows — reads as instant. A spinner reads as broken.

Streaming partial input

app.ontoolinputpartial = (partial) => updatePreview(partial);

The host streams JSON-healed partial arguments as the model produces them. Two rules:

Treat it as incomplete. Healed JSON can contain truncated strings and missing keys. Render it; never act on it, never send it to a server.
Have a fallback. M365 Copilot does not support it, so ontoolinput must produce an acceptable skeleton on its own.

Do not remount

Remount is the most expensive thing that can happen and the easiest to cause.

The cause, nearly always: a UI resource attached to a tool the model calls repeatedly. The fix is the decoupled data/render split —

Data tools: fetch, compute, mutate. No resourceUri.
Render tools: take prepared data, carry resourceUri.

Then let the View call data tools directly for local interactions ("Re-roll", "Next page", "Refresh"). The widget updates in place; nothing remounts.

Mark those direct-call tools visibility: ["app"] so they stay out of the model's tool list and out of the conversation context.

Round trips

Every callServerTool is View → host → server → host → View. Budget for it.

Batch. One tool that returns a page of results beats ten that return one each.
Debounce user-driven calls (search-as-you-type: 250–400 ms).
Render optimistically where the operation is safe to roll back, but do not push optimistic values into updateModelContext before the server confirms.
Cache immutable lookups in View memory for the instance's lifetime.

Polling

For live data, poll with an app-only tool and always clean up:

const id = setInterval(async () => {
  const r = await app.callServerTool({ name: "get_status", arguments: { jobId } });
  render(r.structuredContent);
}, 5000);

app.onteardown = () => clearInterval(id);

onteardown is unsupported in M365 Copilot. Pair it with a visibility check so an orphaned View does not poll forever:

if (document.hidden) return;   // inside the interval body

Back off on errors — a failing endpoint polled every 5 s is a self-inflicted outage.

Chunked transfer

Hosts cap message size. For large payloads (files, images, long exports), register an app-only tool returning paginated base64 chunks with offset, totalBytes, and hasMore, and reassemble in the View. Show progress; users tolerate slow far better than frozen.

Offscreen work

A View scrolled out of the conversation still runs.

const io = new IntersectionObserver(([entry]) => {
  entry.isIntersecting ? resumeExpensiveWork() : pauseExpensiveWork();
});
io.observe(document.body);

Pause animation loops, polling, video, and canvas rendering. Combine with document.hidden for tab-level backgrounding.

Size changes

sendSizeChanged / notifyIntrinsicHeight drives host layout. Emitting it on every frame produces visible thrash and, with some hosts, a resize feedback loop.

Emit on content change, not on every render.
Use ResizeObserver and debounce (~100 ms).
Round to integers; sub-pixel deltas cause oscillation.
Never call it from inside a handler that fires because of a resize.

Rendering

Virtualize lists past ~100 rows.
Bound what you render: cap array length at the parse boundary, paginate the rest.
Keep animation to transform and opacity; honor prefers-reduced-motion.
Avoid layout thrash — read all measurements, then write all styles.
Memoize expensive derived data; the tool result is usually stable between renders.

Measuring

performance.mark("app-connect");
app.ontoolresult = () => {
  performance.mark("first-render");
  app.sendLog?.({
    level: "info",
    data: performance.measure("ttfr", "app-connect", "first-render").duration,
  });
};

Track: time to first render, bundle size, round trips per interaction, remounts per conversation. The last one should almost always be 1.

Checklist

openai-apps-sdk — the decoupled tool pattern in full.
mcp-apps-sdk — build configuration.
widget-ux-patterns — loading, empty, and error states.

ui-performance