Asana — Guardrails and Fix Patterns

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

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
Idempotency
Build dedupe_key = sha256(task.gid + wf_rev + index_hash) and store it (custom field or external KV).
Drop duplicates on retries.
RAG boundary contract
Always pass snippet_id, section_id, source_url, offsets, tokens, project_gid.
Enforce cite-then-explain. Specs:
Retrieval Traceability · Data Contracts
Observability probes
Log ΔS(question, retrieved) and λ per stage; alert on ΔS ≥ 0.60 or divergent λ.
Overview map:
RAG Architecture & Recovery
Single writer
Route task/comments/field updates through one writer branch or one integration user with a mutex.
See:
Deployment Deadlock
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

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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