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Microsoft Power Automate — Guardrails and Fix Patterns

🧭 Quick Return to Map

You are in a sub-page of Automation Platforms.
To reorient, go back here:

Think of this page as a desk within a ward.
If you need the full triage and all prescriptions, return to the Emergency Room lobby.

Use this when your workflow is built with Power Automate (cloud flows, AI Builder, custom connectors) and you see wrong citations, unstable answers, mixed sources, or silent failures that “look green” in run history.

Acceptance targets

  • ΔS(question, retrieved) ≤ 0.45
  • Coverage ≥ 0.70 to the intended section/record
  • λ remains convergent across 3 paraphrases

Typical breakpoints → exact fixes

Minimal Power Automate pattern with WFGY checks

Below is a compact flow outline. It enforces cite-first schema, observable retrieval, and a ΔS/λ post-check.

Trigger: When an HTTP request is received
Actions:
1) Initialize variable "k" = 10
2) Parse JSON "question" from request
3) HTTP → your retriever endpoint
   - Method: POST
   - Body: { "question": "@{variables('question')}", "k": "@{variables('k')}" }
4) Compose "context" = join(retrieved.snippets)
5) Compose "prompt" =
   SYSTEM: Cite lines before any explanation.
   TASK: Answer the user's question using the provided context.
   CONSTRAINTS:
   - Do not mix sources
   - Provide snippet_id for each citation
   CONTEXT:
   @{outputs('Compose_context')}
   QUESTION:
   @{variables('question')}
6) AI Builder / Custom Connector → LLM with "prompt"
7) HTTP → wfgyCheck (custom Azure Function)
   - Body: { "question": "@{variables('question')}",
             "context": "@{outputs('Compose_context')}",
             "answer": "@{outputs('LLM_action')}" }
8) Condition:
   If deltaS ≥ 0.60 OR lambda != "→"
      → Terminate flow (Warn) "High semantic stress. See trace log."
   Else
      → Return 200 with { answer, deltaS, lambda, coverage, citations[] }

What this enforces

  • Retrieval parameters are explicit and logged in flow run details.
  • Prompt is schema-locked with cite-first.
  • WFGY check runs after generation and can fail fast when ΔS is high or λ flips divergent.
  • Trace table (snippet_id ↔ citation) is returned for audit.

Reference specs RAG Architecture & Recovery · Retrieval Playbook · Retrieval Traceability · Data Contracts

Power Automate specific gotchas

  • Environment or connection drift: different Dataverse/SharePoint connections between ingestion and query. Pin connections per environment and re-verify secrets. See Pre-Deploy Collapse

  • Throttling/parallel branches change ordering of records. Add a rerank stage only after per-source ΔS ≤ 0.50. See Rerankers

  • Parse JSON actions silently drop fields, breaking snippet_id propagation. Validate schemas and keep snippet_id mandatory. See Retrieval Traceability

  • Embedding metric mismatch between ingestion code (Azure Function/Logic App) and query side. Normalize vectors and pin cosine vs. inner product. See Embedding ≠ Semantic

  • Scheduled flows rebuild indices unintentionally. Make builds idempotent and gate by boot checks. See Bootstrap Ordering

When to escalate

  • ΔS remains ≥ 0.60 after chunking and retrieval fixes Work through the playbook, then rebuild the index with explicit metric flags and unit normalization. Retrieval Playbook

  • Answers flip between Dev/UAT/Prod Verify version skew, connection references, and secrets. Pre-Deploy Collapse

🔗 Quick-Start Downloads (60 sec)

Tool Link 3-Step Setup
WFGY 1.0 PDF Engine Paper 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>”
TXT OS (plain-text OS) TXTOS.txt 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

Explore More

Layer Page What it’s for
⭐ Proof WFGY Recognition Map External citations, integrations, and ecosystem proof
⚙️ Engine WFGY 1.0 Original PDF tension engine and early logic sketch (legacy reference)
⚙️ Engine WFGY 2.0 Production tension kernel for RAG and agent systems
⚙️ Engine WFGY 3.0 TXT based Singularity tension engine (131 S class set)
🗺️ Map Problem Map 1.0 Flagship 16 problem RAG failure taxonomy and fix map
🗺️ Map Problem Map 2.0 Global Debug Card for RAG and agent pipeline diagnosis
🗺️ Map Problem Map 3.0 Global AI troubleshooting atlas and failure pattern map
🧰 App TXT OS .txt semantic OS with fast bootstrap
🧰 App Blah Blah Blah Abstract and paradox Q&A built on TXT OS
🧰 App Blur Blur Blur Text to image generation with semantic control
🏡 Onboarding Starter Village Guided entry point for new users

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