CrewAI: Guardrails and Fix Patterns

🧭 Quick Return to Map

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

Agents & Orchestration — orchestration frameworks and guardrails

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 orchestration uses CrewAI (agents, tasks, tools, crews, planning) and you see tool loops, wrong snippets, role mixing, or answers that flip between runs. The table maps symptoms to exact WFGY fix pages and gives a minimal recipe you can paste.

Acceptance targets

ΔS(question, retrieved) ≤ 0.45
Coverage ≥ 0.70 to the intended section or record
λ stays convergent across 3 paraphrases and 2 seeds
E_resonance stays flat on long windows

Open these first

Visual map and recovery
RAG Architecture & Recovery
End to end retrieval knobs
Retrieval Playbook
Why this snippet
Retrieval Traceability
Ordering control
Rerankers
Embedding vs meaning
Embedding ≠ Semantic
Hallucination and chunk edges
Hallucination
Long chains and entropy
Context Drift · Entropy Collapse
Structural collapse and recovery
Logic Collapse
Prompt injection and schema locks
Prompt Injection
Multi agent conflicts
Multi-Agent Problems
Bootstrap and deployment ordering
Bootstrap Ordering · Deployment Deadlock · Pre-Deploy Collapse
Snippet and citation schema
Data Contracts

Typical breakpoints and the right fix

Agent to agent handoff loops or stalls
Add BBCR bridge steps, set explicit timeouts, log λ per hop, clamp variance with BBAM.
Open: Logic Collapse · Multi-Agent Problems
High similarity yet wrong meaning
Mixed write and read embeddings, metric mismatch, or fragmented stores.
Open: Embedding ≠ Semantic · Vectorstore Fragmentation
Hybrid retrieval performs worse than a single retriever
Two stage query drifts, mis weighted rerank, inconsistent analyzer.
Open: Query Parsing Split · Rerankers
Citations missing or inconsistent across agents
Require cite then explain and lock snippet fields at the task boundary.
Open: Retrieval Traceability · Data Contracts
Planner injects unsafe tool prompts
Freeze tool schemas and validate arguments before execution.
Open: Prompt Injection
Long runs flatten style and drift logically
Split tasks, re join with BBCR, measure entropy and stop when it rises.
Open: Context Drift · Entropy Collapse

Fix in 60 seconds

Measure ΔS
Compute ΔS(question, retrieved) and ΔS(retrieved, expected anchor).
Stable < 0.40, transitional 0.40 to 0.60, risk ≥ 0.60.
Probe λ_observe
Do a k sweep in retrieval and reorder prompt headers. If λ flips, lock the schema and clamp with BBAM.
Apply the module

Retrieval drift → BBMC plus Data Contracts
Reasoning collapse → BBCR bridge plus BBAM, verify with Logic Collapse
Hallucination re entry after correction → Pattern: Hallucination Re-entry

Verify
Coverage ≥ 0.70. ΔS ≤ 0.45. Three paraphrases and two seeds with λ convergent.

Minimal CrewAI pattern with WFGY checks

# Pseudocode: highlight the control points only
from crewai import Agent, Task, Crew

def retrieve_snippets(q):
    # unified analyzer and metric across dense and sparse
    return retriever.search(q, k=10)

def assemble_prompt(context, q):
    # schema-locked prompt with cite first
    return prompt.format(context=context, question=q)

def wfgy_checks(q, context, answer):
    # compute ΔS(question, context) and enforce thresholds
    # record snippet_id, section_id, source_url, offsets, tokens
    metrics = metrics_and_trace(q, context, answer)
    if metrics["risk"]:
        raise RuntimeError("WFGY gate: high ΔS or divergent λ")
    return metrics

researcher = Agent(
    role="retrieval",
    goal="fetch auditable snippets with fields locked",
    backstory="RAG specialist who always cites first"
)

writer = Agent(
    role="reasoning",
    goal="answer with cite then explain using the snippet schema",
    backstory="keeps λ convergent and avoids cross section reuse"
)

task_retrieve = Task(
    description="Retrieve k=10 with unified analyzer, return snippet schema",
    agent=researcher,
    expected_output="list of snippets with {snippet_id, section_id, source_url, offsets, tokens}"
)

task_answer = Task(
    description="Assemble cite-first prompt and answer with strict JSON",
    agent=writer,
    expected_output="{citations:[...], answer:'...'}"
)

crew = Crew(agents=[researcher, writer], tasks=[task_retrieve, task_answer])

def run(question):
    context = retrieve_snippets(question)
    msg = assemble_prompt(context, question)
    answer = crew.kickoff(inputs={"msg": msg})
    metrics = wfgy_checks(question, context, answer)
    return {"answer": answer, "metrics": metrics}

What this enforces

Retrieval is observable and parameterized. Analyzer and metric are unified.
Prompt is schema locked with cite first and strict JSON for tool outputs.
A post generation WFGY gate can halt the run when ΔS is high or λ flips.
Traces record snippet to citation mapping for audits.

Specs and recipes RAG Architecture & Recovery · Retrieval Playbook · Retrieval Traceability · Data Contracts

CrewAI specific gotchas

Mixed embedding functions across write and read. Rebuild with explicit metric and normalization. See Embedding ≠ Semantic
Planner emits tool prompts that bypass the schema. Always validate tool arguments and echo the schema every step. See Prompt Injection
Memory overwrite between agents. Stamp mem_rev and mem_hash, split namespaces by agent role. See role drift · memory desync
Event storms when multiple tasks write to the same index or KV. Add idempotency keys on {source_id, mem_rev, index_hash}. See Retrieval Traceability
Long runs degrade style and flip answers. Split the plan, then re join with a BBCR bridge and clamp with BBAM. See Context Drift

When to escalate

ΔS remains ≥ 0.60 Rebuild the index using the checklists and verify with a small gold set. Retrieval Playbook
Identical input yields different answers across runs Check version skew and session state. 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 →

👑 Early Stargazers: See the Hall of Fame Engineers, hackers, and open source builders who supported WFGY from day one.

⭐ WFGY Engine 2.0 is already unlocked. ⭐ Star the repo to help others discover it and unlock more on the Unlock Board.

14 KiB Raw Blame History Unescape Escape