If you’re wiring LLMs, RAG, or agents into n8n, Make (Integromat), or Zapier, you’ve probably seen flows “work” and then mysteriously fall apart in production. This post is a practical catalog of failure modes and fixes you can apply today.
I maintain a public, MIT-licensed ProblemMap (16 common failure patterns with concrete remedies). It’s been battle-tested on Reddit with tons of positive feedback, and the repo has been growing fast (also starred by the creator of Tesseract.js).
👉 ProblemMap: https://github.com/onestardao/WFGY/blob/main/ProblemMap/README.md
Who this helps
- Builders connecting LLMs/agents to n8n / Make / Zapier
- Teams adding RAG (FAISS/Pinecone/Weaviate/etc.)
- Anyone tired of “it works on my machine” pipelines
The 16 Failure Modes (mapped to automation reality)
Below are concise definitions + symptoms in n8n/Make/Zapier + guardrails you can implement quickly. Use this to spot your issue fast, then jump to the ProblemMap for step-by-step fixes.
1) Hallucination & Chunk Drift
Symptoms: RAG answers look fluent but cite irrelevant or stale chunks.
Guardrails: document freshness checks, metadata filters, retrieval sanity tests before LLM call.
2) Interpretation Collapse
Symptoms: The input is correct, but logic in subsequent nodes misreads intent.
Guardrails: schema validators, explicit intent fields, small unit prompts instead of one giant prompt.
3) Long Reasoning Chains
Symptoms: Multi-step flows degrade each hop; answers diverge.
Guardrails: critic/reviser step, max-depth caps, checkpointing intermediate facts.
4) Bluffing / Overconfidence
Symptoms: “Looks” confident, returns wrong or unverifiable claims.
Guardrails: require sources, add refusal rules, route low-confidence answers to human review.
5) Semantic ≠ Embedding
Symptoms: Good vector scores, wrong meaning (tokenizer/norm mismatch).
Guardrails: lock same tokenizer, normalization and dims for build+query; block mixed models.
6) Logic Collapse & Recovery
Symptoms: Flow passes, but a branch silently short-circuits (wrong condition order, partial data).
Guardrails: pre-flight assertions on required fields; rollback & retry policy; “must-pass” gates.
7) Memory Breaks Across Sessions
Symptoms: Agent forgets context between nodes or runs.
Guardrails: durable memory store with keys per conversation; explicit merge & TTL policies.
8) Debugging Is a Black Box
Symptoms: Unit tests call live APIs; flakey CI; non-reproducible failures.
Guardrails: mock LLM/API in unit tests; push live calls to integration tests; seed fixed local models.
9) Entropy Collapse (Prompt Injection / Jailbreaks)
Symptoms: User input alters system behavior or leaks secrets; downstream tools misfire.
Guardrails: input isolation, policy prompts, tool-call whitelists, red-team tests before release.
10) Creative Freeze
Symptoms: Model gets overly literal; zero useful synthesis.
Guardrails: diversify few-shot examples; temperature ranges with fallback.
11) Symbolic Collapse
Symptoms: Regex/DSL/code-gen steps intermittently break; small syntax changes wreck the chain.
Guardrails: strict parsers, contracts, and error-aware retries; treat code output as untrusted input.
12) Philosophical Recursion
Symptoms: Self-referential loops (“explain the plan to improve the plan…”) stall flows.
Guardrails: loop counters, termination proofs, hard caps, and periodic human breakpoints.
13) Multi-Agent Chaos
Symptoms: Agents overwrite each other’s state; handoffs are lost.
Guardrails: single source of truth, explicit ownership per phase, idempotent writes, event logs.
14) Bootstrap Ordering
Symptoms: Orchestration fires before retriever/index/cache is ready; first runs look “broken.”
Guardrails: gate first query on ready status; warm caches; purge stale indexes on swaps.
15) Deployment Deadlock
Symptoms: Circular waits (DB migrator vs. index builder vs. app), queues jam.
Guardrails: startup probes, sequential init with timeouts, health checks per dependency.
16) Pre-Deploy Collapse
Symptoms: You upload docs, immediately query, get empty/partial matches (indexing not done).
Guardrails: explicit ingestion status, “indexing…” UX state + queued question, auto-retry.
Quick Triage for n8n / Make / Zapier
- Reproduce locally with fixed seeds; mock external APIs for unit testing (No.8).
- Check readiness: is your vector store / cache warm? (No.14, No.16).
- Lock embeddings: same dims/tokenizer/norm across build+query (No.5).
- Add gates: assertions for required fields before expensive LLM calls (No.6).
- Harden prompts: input isolation & tool whitelists (No.9).
- Audit handoffs: single writer per state; append-only logs (No.13).
- Smoke tests: exact-match, paraphrase, and constraint queries before you ship.
Platform-specific tips
n8n
- Gate the first LLM node on an ingestion-ready flag (No.14/16).
- Use separate credentials for write vs read nodes to limit blast radius (No.9).
- Add a Result Check node after vector search: empty/near-zero scores trigger a fallback path (No.1/5).
Make (Integromat)
- Iterator + Array Aggregator paths: assert expected counts and types to avoid silent short-circuits (No.6/11).
- Use routers for “human-in-the-loop” on low confidence; store the verdict for reuse (No.4/10).
Zapier
- Long zaps: ensure tokens refresh mid-flow; retry on 401 with backoff (No.4/5/15).
- For web-hooks triggering RAG: queue the user’s question if indexing still running (No.16).
Example “Ready Gate” (pseudo-logic)
IF vector_index.status != "ready":
enqueue(user_query)
return "Indexing… I’ll run your question the moment it’s ready."
ELSE:
result = retrieve(user_query)
if result is empty: fallback_search()
This tiny guard removes a huge class of flaky first-run bugs (No.14/16).
Why trust this map?
- Open-source, MIT.
- Endorsed by the Tesseract.js creator.
- Proven with fast GitHub star growth and lots of real-world fixes from devs who tried it the same day on Reddit.
Again, the full reference with all 16 patterns and remedies is here:
https://github.com/onestardao/WFGY/blob/main/ProblemMap/README.md
If you want a focused checklist for your stack (n8n/Make/Zapier), ping me and tell me which symptoms you’re seeing — I’ll point you to the exact problem number and the fastest fix.
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