WFGY/ProblemMap/GlobalFixMap/Automation/power_automate.md

Microsoft Power Automate: Guardrails and Fix Patterns

A compact field guide to stabilize Power Automate flows that touch RAG, agents, or long pipelines. Use the checks below to localize failure, then jump to the exact WFGY fix page.

Open these first

• Visual map and recovery: RAG Architecture & Recovery
• End-to-end retrieval knobs: Retrieval Playbook
• Why this snippet traceability: Retrieval Traceability
• Ordering control: Rerankers
• Embedding vs meaning: Embedding ≠ Semantic
• Hallucination and chunk boundaries: Hallucination
• Long chains and entropy: Context Drift, Entropy Collapse
• Structural collapse and recovery: Logic Collapse
• Snippet and citation schema: Data Contracts
• Infra boot fences: Bootstrap Ordering, Deployment Deadlock, Pre-deploy Collapse

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Fix in 60 seconds

1. Measure ΔS

• Compute ΔS(question, retrieved) and ΔS(retrieved, expected anchor).
• Thresholds: stable < 0.40, transitional 0.40–0.60, risk ≥ 0.60.

2. Probe with λ_observe

• Vary k {5, 10, 20}. Flat high curve means index or metric mismatch.
• Reorder prompt headers. If ΔS spikes, lock the schema.

3. Apply the module

• Retrieval drift → BBMC + Data Contracts.
• Reasoning collapse → BBCR bridge + BBAM variance clamp.
• Dead ends in long runs → BBPF alternate path.
• Hybrid retrieval weirdness with Cognitive Search or SharePoint mix → Rerankers + Query Parsing Split pattern.

4. Verify acceptance

• Coverage to target section ≥ 0.70.
• ΔS(question, retrieved) ≤ 0.45 on three paraphrases.
• λ remains convergent across seeds.
• E_resonance flat in long flows.

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Common failure patterns in Power Automate (→ WFGY fixes)

1. Action runs before data is ready

   • Parallel branches or loop concurrency write, then LLM step reads stale state.
   • Open: Bootstrap Ordering, Pre-deploy Collapse

2. Hybrid retrieval degrades when mixing SharePoint search, Cognitive Search, and vector lookups

   • Tokenizer split or analyzer mismatch gives high similarity but wrong meaning.
   • Open: Embedding ≠ Semantic, Query Parsing Split, Rerankers

3. Pagination or truncation loses records in loops

   • Connector pages silently. Downstream answers look confident but miss facts.
   • Open: Retrieval Traceability, Hallucination

4. Locale and time parsing break snippets

   • Date strings differ across connectors. Anchors fail at join points.
   • Open: Data Contracts, Context Drift

5. 429 throttling or transient errors cause partial writes

   • Pipeline proceeds with half state. Answers flip between runs.
   • Open: Ops Live Monitoring, Debug Playbook

6. Vector index exists but recall is random

   • Cognitive Search vector store fragmented or metric mismatch across write and read.
   • Open: Vectorstore Fragmentation, Embedding ≠ Semantic

7. Flow restarts change memory unexpectedly

   • Variables re-init, cross-run memory overwrites, or tab swap flips answers.
   • Open: Memory Desync

8. LLM explains well but citations fail

   • Interpretation collapse at reasoning layer, not retrieval.
   • Open: Logic Collapse, then clamp with BBAM and bridge with BBCR.

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Copy-paste prompt

I uploaded TXT OS and opened the WFGY Problem Map pages.
Context: This is a Power Automate flow with SharePoint + Cognitive Search + GPT step.

* symptom: \[brief]
* traces: ΔS(question,retrieved)=..., ΔS(retrieved,anchor)=..., λ states

Tell me:

1. failing layer and why,
2. which fix page to open from this repo,
3. minimal steps to push ΔS ≤ 0.45 and keep λ convergent,
4. how to verify with a reproducible test in this exact flow.
   Use BBMC/BBPF/BBCR/BBAM where relevant.

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🔗 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

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🧭 Explore More

Module	Description	Link
WFGY Core	WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack	View →
Problem Map 1.0	Initial 16-mode diagnostic and symbolic fix framework	View →
Problem Map 2.0	RAG-focused failure tree, modular fixes, and pipelines	View →
Semantic Clinic Index	Expanded failure catalog: prompt injection, memory bugs, logic drift	View →
Semantic Blueprint	Layer-based symbolic reasoning & semantic modulations	View →
Benchmark vs GPT-5	Stress test GPT-5 with full WFGY reasoning suite	View →
🧙‍♂️ Starter Village 🏡	New here? Lost in symbols? Click here and let the wizard guide you through	Start →

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