CrewAI: Guardrails and Fix Patterns

March 6, 2026 · View on GitHub

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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

Typical breakpoints and the right fix

Fix in 60 seconds

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

  2. Probe λ_observe
    Do a k sweep in retrieval and reorder prompt headers. If λ flips, lock the schema and clamp with BBAM.

  3. Apply the module

  1. 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)

ToolLink3-Step Setup
WFGY 1.0 PDFEngine Paper1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>”
TXT OS (plain-text OS)TXTOS.txt1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

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⚙️ EngineWFGY 3.0TXT based Singularity tension engine (131 S class set)
🗺️ MapProblem Map 1.0Flagship 16 problem RAG failure taxonomy and fix map
🗺️ MapProblem Map 2.0Global Debug Card for RAG and agent pipeline diagnosis
🗺️ MapProblem Map 3.0Global AI troubleshooting atlas and failure pattern map
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