Delegation in Canvas Apps | Designing for Performance | Rahsi Framework™

This is not about correcting Microsoft. It’s about explaining Microsoft’s design philosophy: scale comes from designed behavior, not late-stage tuning.

In Canvas Apps, the real performance constitution is delegation: when your Power Fx formula shape can be delegated, the data source executes the narrowing and the app stays calm. When it can’t, evaluation shifts client-side by design—and row limits, payload width, network latency, CPU, and render cost start writing the story across different execution contexts.

My RAHSI™ lens is simple:

execution context → trust boundary → formula shape → delegation → data call flow → payload discipline → query patterns → caching intent → Monitor evidence → proof window

If you can answer three questions in one timebox, you can stabilize most “scale oscillation” conversations—quietly, with evidence:

• What exact formula shape is being executed?
• What did the data source actually execute (delegated vs client-evaluated)?
• What does Monitor show for the same window: calls, timings, payloads, and rendering?

The best part: when the trust boundary is deterministic, your operational language stays consistent—even down to how Copilot honors labels in practice inside governed environments.

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RAHSI™ Delegation Proof Pack (One Timebox, One Narrative)

Plane (RAHSI™)	What you lock down	What you collect as proof	Designed behavior signal
Execution context	Environment, user cohort, device/network, peak concurrency	Timebox + scope notes + test path	“Same window, same story”
Trust boundary	Entra roles, connector permissions, DLP posture, data access scope	Change log + ownership map	“Who can change what is explicit”
Formula shape	Exact Items formulas for galleries/search/sort	Formula inventory (per screen)	“Shape is intentional”
Delegation outcome	What delegated vs what evaluated client-side	Delegation warnings + Monitor evidence	“Server does narrowing”
Data call flow	Connector route (OData/API), calls per interaction	Monitor call list + timings	“Call fan-out is controlled”
Payload discipline	Columns returned, payload width, heavy fields avoided	Evidence of selected columns + response sizes	“Narrow payload, fast render”
Query patterns	Views, indexed fields, predictable sort/filter	Pattern notes + datasource alignment	“Stable query path”
Caching intent	Why cache exists, what it holds, refresh cadence	Collection design + refresh triggers	“Cache reduces repeated calls”
Monitor evidence	Single truth of runtime behavior	Monitor trace for the window	“Replayable proof”
Proof window	One-page narrative summary	Links, screenshots, export	“Stakeholder-grade clarity”

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Delegation in Canvas Apps | Designing for Performance | Rahsi Framework™

Delegation in Canvas Apps | Designing for Performance | Rahsi Framework™: delegate Fx, shrink payloads, validate in Monitor ship at scale2x.

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