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WFGY/ProblemMap/GlobalFixMap/Automation/asana.md

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Asana — 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 page when your RAG or agent flow is orchestrated through Asana (rules, webhooks, custom apps) and behavior drifts: tasks duplicate, sections race, or citations don’t match the source.

Acceptance targets

  • ΔS(question, retrieved) ≤ 0.45
  • coverage ≥ 0.70 to the target section
  • λ remains convergent across 3 paraphrases

Typical breakpoints → exact fixes

  • Webhooks or rules trigger before data/index is actually ready
    Fix No.14: Bootstrap Ordering →
    Bootstrap Ordering

  • First run after deploy crashes or uses the wrong secret
    Fix No.16: Pre-Deploy Collapse →
    Pre-Deploy Collapse

  • Parallel automations create circular waits (task → comment → external job → task)
    Fix No.15: Deployment Deadlock →
    Deployment Deadlock

  • High cosine similarity but the retrieved text is not the intended meaning
    Fix No.5: Embedding ≠ Semantic →
    Embedding ≠ Semantic

  • Citations don’t match snippets or “why this snippet?” is opaque
    Fix No.8: Retrieval Traceability →
    Retrieval Traceability
    Standardize payloads with Data Contracts →
    Data Contracts

  • Hybrid retrieval via external tools performs worse than single retriever
    Pattern: Query Parsing Split →
    Query Parsing Split
    Also review Rerankers →
    Rerankers

  • Facts are indexed but never surface
    Pattern: Vectorstore Fragmentation →
    Vectorstore Fragmentation

  • Status rollups blend two sources into one narrative
    Pattern: Symbolic Constraint Unlock (SCU) →
    Symbolic Constraint Unlock

Minimal Asana workflow checklist

  1. Warm-up fence
    Before any LLM step, call a health endpoint that verifies VECTOR_READY, INDEX_HASH, secret_rev.
    If not ready, delay/requeue. Spec:
    Bootstrap Ordering

  2. Idempotency
    Build dedupe_key = sha256(task.gid + wf_rev + index_hash) and store it (custom field or external KV).
    Drop duplicates on retries.

  3. RAG boundary contract
    Always pass snippet_id, section_id, source_url, offsets, tokens, project_gid.
    Enforce cite-then-explain. Specs:
    Retrieval Traceability · Data Contracts

  4. Observability probes
    Log ΔS(question, retrieved) and λ per stage; alert on ΔS ≥ 0.60 or divergent λ.
    Overview map:
    RAG Architecture & Recovery

  5. Single writer
    Route task/comments/field updates through one writer branch or one integration user with a mutex.
    See:
    Deployment Deadlock

  6. Regression gate
    Require coverage ≥ 0.70 and ΔS ≤ 0.45 before posting summaries back to Asana.
    Eval spec:
    RAG Precision/Recall

Copy-paste prompt for the Asana LLM step


I uploaded TXT OS and the WFGY Problem Map files.
This Asana flow retrieved {k} snippets with fields {snippet\_id, section\_id, source\_url, offsets, project\_gid}.
Question: "{user\_question}"

Do:

1. Enforce cite-then-explain. If any citation is missing, stop and return which fix page to open.
2. Compute ΔS(question, retrieved). If ΔS ≥ 0.60, point me to the minimal structural fix:
   retrieval-playbook, retrieval-traceability, data-contracts, rerankers.
3. Output compact JSON:
   { "citations": \[...], "answer": "...", "λ\_state": "→|←|<>|×", "ΔS": 0.xx, "next\_fix": "..." }

Common Asana gotchas

  • Webhook retries produce duplicate tasks/comments
    Use the dedupe_key and a KV lock; make writes idempotent.

  • Project/section renames break downstream references
    Pass project_gid and section_id explicitly in the data contract.

  • Parallel rules edit the same fields
    Funnel edits through a single writer branch or queue.

  • External rate limits destabilize hybrids
    Prefer dense retriever + reranking, keep per-retriever params logged.

When to escalate

  • ΔS remains ≥ 0.60 after chunk/retrieval fixes → rebuild index with explicit metric/normalization.
    See:
    Retrieval Playbook

  • Same input yields different answers across runs → check version skew and memory desync.
    See:
    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 based tension engine
Engine WFGY 2.0 Production tension kernel and math engine for RAG and agents
Engine WFGY 3.0 TXT based Singularity tension engine, 131 S class set
Map Problem Map 1.0 Flagship 16 problem RAG failure checklist and fix map
Map Problem Map 2.0 RAG focused recovery pipeline
Map Problem Map 3.0 Global Debug Card, image as a debug protocol layer
Map Semantic Clinic Symptom to family to exact fix
Map Grandma’s Clinic Plain language stories mapped to Problem Map 1.0
Onboarding Starter Village Guided tour for newcomers
App TXT OS TXT semantic OS, fast boot
App Blah Blah Blah Abstract and paradox Q and A built on TXT OS
App Blur Blur Blur Text to image with semantic control
App Blow Blow Blow Reasoning game engine and memory demo

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