Intercom: Guardrails and Fix Patterns

🧭 Quick Return to Map

You are in a sub-page of Chatbots & CX.
To reorient, go back here:

• Chatbots & CX — customer dialogue flows and conversational stability
• WFGY Global Fix Map — main Emergency Room, 300+ structured fixes
• WFGY Problem Map 1.0 — 16 reproducible failure modes

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 when your Intercom bot blends Fin (AI Agent), Custom Bots, Help Center articles, and webhooks hitting your RAG stack. The checks localize failures to the exact layer and jump you to the right WFGY fix page. All links are text-hyperlinks, absolute to GitHub.

Open these first

• Visual map and recovery: rag-architecture-and-recovery.md
• End-to-end retrieval knobs: retrieval-playbook.md
• Traceability: retrieval-traceability.md
• Data schema locks: data-contracts.md
• Embedding vs meaning: embedding-vs-semantic.md
• Hallucination and chunk boundaries: hallucination.md
• Long chains and entropy: context-drift.md, entropy-collapse.md
• Prompt injection and tool schema: prompt-injection.md
• Multi-agent handoffs: Multi-Agent_Problems.md
• Boot order traps: bootstrap-ordering.md, deployment-deadlock.md, predeploy-collapse.md

Core acceptance (CX)

• ΔS(question, retrieved) ≤ 0.45
• Coverage ≥ 0.70 to the target section
• λ remains convergent across 3 paraphrases and 2 seeds
• E_resonance stays flat over long sessions

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Fix in 60 seconds

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

2. Probe λ_observe Vary k and reorder prompt headers. If λ flips on harmless paraphrases, lock schema and clamp with BBAM.

3. Apply module

   • Retrieval drift → BBMC + retrieval-traceability.md + data-contracts.md
   • Reasoning collapse in long chats → BBCR bridge + BBAM; verify with context-drift.md
   • Dead ends in toolchains → BBPF alternate paths

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

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Typical Intercom symptoms → exact fix

• Fin answers without citing the right Help Center article Analyzer/metric mismatch or fragmented store feeding Fin. → embedding-vs-semantic.md, patterns/pattern_vectorstore_fragmentation.md

• Resolution Bot hands off to human too early or loops Boot order or version skew between content sync and bot. → bootstrap-ordering.md, deployment-deadlock.md

• Webhook returns 200 but the bot state drifts Tool JSON schema too loose; free-text in arguments. → data-contracts.md, prompt-injection.md

• High similarity, wrong snippet Metric mismatch or hybrid query split between Help Center and external KB. → retrieval-playbook.md, patterns/pattern_query_parsing_split.md

• Long threads become inconsistent after 20–40 turns Entropy rises with chain length; memory writes collide. → context-drift.md, entropy-collapse.md, Multi-Agent_Problems.md

• Jailbreak or confident bluffing Missing fences and cite-then-explain rules. → bluffing.md, retrieval-traceability.md

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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 your retriever with explicit metric and consistent analyzer. Return snippet_id, section_id, source_url, offsets, tokens.

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

4. LLM answer step LLM reads TXT OS and WFGY schema. Enforce cite-then-explain across the retrieved set.

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

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Copy-paste prompt for your Intercom webhook

You have TXT OS and the WFGY Problem Map loaded.

My Intercom context:
- channel: messenger | email | mobile
- bot: Fin | Custom Bot | Resolution Bot
- retrieved: {k} snippets {snippet_id, section_id, source_url, offsets, tokens}

User question: "{user_question}"

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

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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

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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