Spec-Driven Power Platform: Series Overview
A connected methodology for building Power Automate solutions: spec-first authoring, tag-based architecture, notification separation, FetchXML over OData, and programmatic solution packaging.
Spec-driven development on Power Platform is not a single technique. It is a connected set of practices that turn flow authoring into a process AI assistants can execute reliably. This series walks through the parts of that methodology one at a time.
The thesis is simple. Governance is not overhead. It is the prerequisite that makes AI-assisted development possible at scale. Without tags, an AI assistant has no grouping signal. Without specs, it makes wrong assumptions. Without solution packaging knowledge, generated flow JSON sits on disk and never reaches Dataverse. Each “boring” governance practice (naming conventions, documentation in git, solution-aware flows) becomes a force multiplier when AI enters the picture.
The Five Practices
1. Spec-First Development
Specifications belong in git, not in a Word document attached to a SharePoint site. Markdown specs are diffable, reviewable in pull requests, and machine-readable. When the spec is precise enough, an AI assistant can generate Power Automate JSON directly from it without opening the designer.
What a machine-readable spec looks like: flow inventories, trigger definitions, FetchXML queries, recipient rules, and email templates, all in markdown with consistent structure.
Read: Spec-First Power Automate Development
2. Tag-Based Flow Architecture
A flat list of two dozen flows is unmanageable. Three-letter prefixes turn that flat list into a navigable architecture. Tags map to work item areas, architecture diagrams, source control folders, and AI agent batches. When you say “build the NTF-EMAIL flows,” the AI knows the scope, the patterns, and the architectural constraints without further explanation.
Read: Tag-Based Flow Architecture
3. Notification Architecture Separation
Notification flows never write to Dataverse. They read records, build email bodies, send mail, and exit. Business flows never send notifications. The two concerns ship in separate flows, separate solutions, and separate run histories.
This separation has two payoffs. A notification failure never breaks the business process. A business flow update never requires changes to notifications. The principle is strict, and the article explains why making it strict is what keeps both sides reliable.
Read: Notification Architecture That Cannot Break Your Business Logic
4. FetchXML Over OData $filter
OData $filter breaks the moment you need temporal queries (records older than a relative window), linked entity joins, or multi-value filters. FetchXML handles all three. The article walks through real query patterns with full comparison tables, including the recipient-grouping pattern that consolidates per-person digests.
Read: FetchXML in Power Automate
5. Programmatic Solution ZIP Packaging
Microsoft documents solution export through the maker portal UI. The internal ZIP structure is not documented. This article fills that gap. It covers the two flow JSON formats (PA Editor format vs solution export format), deterministic UUIDv5 GUIDs for repeatable builds, the forward-slash path-separator trap that causes silent import failures on Windows, and a complete Node.js packaging pipeline.
Read: Building Dataverse Solution ZIPs Programmatically
Optional: AI-Assisted Parallel Build
The five practices above are the methodology. With them in place, AI-assisted parallel build becomes practical. Multiple AI agent threads can each take a tag-prefixed batch, generate flow JSON in parallel, and produce structurally consistent output because they all read the same spec.
The companion articles cover the patterns and the honest assessment of where AI gets the architecture wrong.
- Spec-Driven Power Automate: Parallel Flow Generation
- What AI Gets Wrong About Power Platform (And Why That Is the Point)
How This Connects to the Governance Series
This series is the application layer for the Power Automate Governance series published earlier on az365.ai. The governance series teaches the practices in the abstract. This series shows how those practices connect into a single methodology that AI assistants can execute against.
| Governance Practice | Spec-Driven Article |
|---|---|
| Naming Conventions That Scale | Tag-Based Flow Architecture |
| Living Documentation in Git | Spec-First Development |
| Solution-Aware Flows | Notification Architecture Separation |
| Versioning and Source Control | Building Solution ZIPs |
| AI-Powered Flow Review | What AI Gets Wrong |
Reading Order
If you are starting from zero on a Power Platform build, read in this order:
- Tag-Based Flow Architecture
- Spec-First Development
- Notification Architecture
- FetchXML in Power Automate
- Building Solution ZIPs
If you already have governance in place and want the AI angle, start with What AI Gets Wrong, then drill into whichever practice you have not yet adopted.
AZ365.ai - Azure and AI insights for architects building on Microsoft. Follow Alex on LinkedIn for architecture deep dives.
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