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WFGY/ProblemMap/GlobalFixMap/Chatbots_CX/dialogflow_cx.md

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Dialogflow CX: Guardrails and Fix Patterns

🧭 Quick Return to Map

You are in a sub-page of Chatbots & CX.
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 page to stabilize Dialogflow CX projects that combine intents, pages, webhooks, knowledge connectors, and LLM calls. The fixes map to WFGY Problem Map pages with measurable targets, so you can verify without changing infra.

Open these first

Core acceptance

  • ΔS(question, retrieved) ≤ 0.45
  • Coverage to the target section ≥ 0.70
  • λ remains convergent across three paraphrases and two seeds
  • E_resonance flat on long windows of dialog turns

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 λ_observe Vary k in retrieval and reorder prompt headers. If λ flips on harmless paraphrases, lock the schema and clamp variance with BBAM.

  3. Apply module

    • Retrieval drift → BBMC + data-contracts.md
    • Reasoning collapse in long flows → BBCR bridge + BBAM, verify with context-drift.md
    • Dead ends in multi-hop tools → BBPF alternate paths

  4. Verify Three paraphrases reach coverage ≥ 0.70 and ΔS ≤ 0.45. λ stays convergent across two seeds.

Typical CX symptoms → exact fix

Symptom Likely cause Open this
Intent matches but the answer cites the wrong section metric mismatch or fragment store behind knowledge connector embedding-vs-semantic.md, patterns/pattern_vectorstore_fragmentation.md
Route group loops between pages or re-prompts bootstrap race or deployment deadlock bootstrap-ordering.md, deployment-deadlock.md
Webhook returns valid JSON yet dialog state degrades across turns schema too loose, missing cite-then-explain, memory writes collide data-contracts.md, retrieval-traceability.md
Knowledge connector high similarity but wrong meaning chunking and anchor mismatch hallucination.md, chunking-checklist.md
Long conversations become inconsistent after 20–40 turns entropy rises with chain length context-drift.md, entropy-collapse.md
Generative fallback overrides citations prompt injection or missing header locks prompt-injection.md, retrieval-traceability.md

CX surface guardrails

Intents and training phrases Keep neutral casing and tokenizer parity with your retriever. If ΔS is flat across k, the issue is metric or index rather than phrases. See retrieval-playbook.md.

Pages and routes Split policy text into system context that never mixes with user turns. Force cite-then-explain on every page that answers. See data-contracts.md.

Webhooks Enforce strict argument schemas and echo them back. Log ΔS, λ_state, INDEX_HASH, snippet_id. If flip states appear, clamp with BBAM. See retrieval-traceability.md.

Knowledge connectors Rebuild with semantic chunking when ΔS stays ≥ 0.60 after reranking. Verify with a small gold set. See embedding-vs-semantic.md.

Live ops Add probes and backoff guards for first-call crashes after deploy. See ops/live_monitoring_rag.md, ops/debug_playbook.md.

Minimal webhook recipe

  1. Warm-up fence Check VECTOR_READY, INDEX_HASH, secrets. Short-circuit if not ready. See bootstrap-ordering.md.

  2. Retrieval step Call retriever with explicit metric. Return snippet_id, section_id, source_url, offsets, tokens.

  3. ΔS probe Compute ΔS(question, retrieved). If ≥ 0.60, set needs_fix=true.

  4. Answer step LLM reads TXT OS and WFGY schema. Enforce cite-then-explain with the retrieved snippet set.

  5. Trace sink Store question, ΔS, λ_state, INDEX_HASH, snippet_id, dedupe_key.

Copy-paste prompt for your CX webhook LLM step

You have TXT OS and the WFGY Problem Map loaded.

My Dialogflow CX context:
- page: {page_name}
- intent: {intent_display_name}
- retrieved: {k} snippets with fields {snippet_id, section_id, source_url, offsets}

Question: "{user_question}"

Tasks:
1) Enforce cite-then-explain. If citations are missing or cross-section, fail fast and return the fix tip.
2) If ΔS(question, retrieved) ≥ 0.60, propose the minimal structural fix. Refer to retrieval-playbook, retrieval-traceability, data-contracts, rerankers.
3) Return JSON:
{ "citations": [...], "answer": "...", "λ_state": "→|←|<>|×", "ΔS": 0.xx, "next_fix": "..." }
Keep it short and auditable.

Test checklist before launch

  • Three paraphrases hit coverage ≥ 0.70 on the same target section.
  • ΔS(question, retrieved) ≤ 0.45 for each.
  • λ remains convergent across two seeds.
  • First-call path after deploy passes warm-up fence.
  • Live probes alert when ΔS ≥ 0.60 or λ flips.

🔗 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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