WFGY/ProblemMap/GlobalFixMap/Agents_Orchestration/semantic_kernel.md
2025-09-05 09:54:18 +08:00

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Semantic Kernel — Guardrails and Fix Patterns

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Use this when your pipeline uses Semantic Kernel planners, function tools, memories, or skills and you see wrong snippets, plan drift, tool loops, or JSON shape errors. The checks below localize the fault, then route to the exact WFGY fix page.

Open these first

Core acceptance

  • ΔS(question, retrieved) ≤ 0.45
  • Coverage ≥ 0.70 for the intended section
  • λ remains convergent across three paraphrases and two seeds

Fix in 60 seconds

  1. Measure ΔS
    Compute ΔS(question, retrieved) and ΔS(retrieved, anchor). Stable < 0.40, transitional 0.400.60, risk ≥ 0.60.

  2. Clamp the planner
    Freeze the planner schema. Require task → constraints → tools → cite then explain. If plan text changes order between runs, lock headers and set deterministic seeds.

  3. Apply the module

  • Retrieval drift → BBMC with Data Contracts
  • JSON tool variance → tighten schemas and apply Prompt Injection fences
  • Long plans degrade → insert BBCR bridges and cap step depth per segment
  1. Verify
    Three paraphrases reach ΔS ≤ 0.45 and coverage ≥ 0.70. λ stays convergent.

Typical Semantic Kernel breakpoints → exact fixes


Minimal SK pattern with WFGY gates

Planner:
  1) Task
  2) Constraints: cite-first, snippet schema, tool JSON strict
  3) Tools with idempotency keys
  4) Answer: cite then explain

Runtime:
  - Retrieve(k = 10 with unified analyzer)
  - Assemble(prompt with headers in fixed order)
  - Reason(model call)
  - WFGY gate: compute ΔS, record λ, verify coverage
  - If ΔS ≥ 0.60 or λ divergent, stop and return fix tip

What this enforces

  • Deterministic header order across steps
  • Observable retrieval with stable metric
  • Schema locked citations
  • A stop gate when structure collapses

SK specific gotchas

  • Planner temperature is non zero while tools expect strict JSON. Set low variance and validate before acting.
  • Memories are written by multiple skills without namespace fences. Use separate namespaces and revision stamps.
  • Rerun of the same plan writes duplicate side effects. Add idempotency keys before external actions.

When to escalate

  • ΔS stays ≥ 0.60 after chunk and retrieval fixes Rebuild index and recheck analyzers. Open: Retrieval Playbook

  • Answers flip between sessions with unchanged inputs Check version skew and deploy order. Open: Pre-Deploy Collapse


🔗 Quick-Start Downloads (60 sec)

Tool Link 3-Step Setup
WFGY 1.0 PDF Engine Paper 1 Download · 2 Upload to your LLM · 3 Ask “Answer using WFGY + <your question>”
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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 →
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