docs: fix human_input oversight claims in security guide

Section 5 of the Security Best Practices guide incorrectly framed
human_input=True as pre-execution approval for irreversible tools.
Clarify that it gates final answer review after tools have run, and
point readers to tool hooks, execution hooks, and @human_feedback for
true pre-execution gates.

Co-authored-by: Rip&Tear <theCyberTech@users.noreply.github.com>

docs/docs.json

@@ -156,7 +156,8 @@
                         "icon": "gear",
                         "pages": [
                           "edge/en/guides/advanced/customizing-prompts",
-                          "edge/en/guides/advanced/fingerprinting"
+                          "edge/en/guides/advanced/fingerprinting",
+                          "edge/en/guides/advanced/security-best-practices"
                         ]
                       },
                       {
@@ -13400,7 +13401,8 @@
                         "icon": "gear",
                         "pages": [
                           "edge/pt-BR/guides/advanced/customizing-prompts",
-                          "edge/pt-BR/guides/advanced/fingerprinting"
+                          "edge/pt-BR/guides/advanced/fingerprinting",
+                          "edge/pt-BR/guides/advanced/security-best-practices"
                         ]
                       },
                       {
@@ -25750,7 +25752,8 @@
                         "icon": "gear",
                         "pages": [
                           "edge/ko/guides/advanced/customizing-prompts",
-                          "edge/ko/guides/advanced/fingerprinting"
+                          "edge/ko/guides/advanced/fingerprinting",
+                          "edge/ko/guides/advanced/security-best-practices"
                         ]
                       },
                       {
@@ -38520,7 +38523,8 @@
                         "icon": "gear",
                         "pages": [
                           "edge/ar/guides/advanced/customizing-prompts",
-                          "edge/ar/guides/advanced/fingerprinting"
+                          "edge/ar/guides/advanced/fingerprinting",
+                          "edge/ar/guides/advanced/security-best-practices"
                         ]
                       },
                       {

docs/edge/ar/guides/advanced/security-best-practices.mdx

@@ -0,0 +1,181 @@
+---
+title: أفضل ممارسات الأمان لـ Agents في CrewAI
+description: إرشادات عملية لتكوين Agents وCrews في CrewAI بأمان في بيئات الإنتاج.
+icon: shield
+---
+
+## نظرة عامة
+
+يركز هذا الدليل على **ضوابط CrewAI الأصلية** التي يمكنك استخدامها لتقليل مخاطر الأمان في أنظمة الإنتاج.
+
+الهدف بسيط: إبقاء سلوك الـ Agent محدودًا، بأقل امتياز، وقابلًا للمراجعة.
+
+## 1) تحديد التنفيذ لمنع السلوك الجامح
+
+استخدم حدود التنفيذ على Agents وCrews حتى تتدهور الأعطال بشكل متوقع بدلًا من التصاعد.
+
+### الضوابط الموصى بها
+
+- `max_rpm`: تحديد معدل الطلبات إلى المزودين
+- `max_iter`: تحديد دورات التفكير/الأدوات التكرارية
+- `max_execution_time`: مهلة صارمة للعمل الطويل
+
+```python
+from crewai import Agent
+
+analyst = Agent(
+    role="Security Analyst",
+    goal="Investigate and summarize incidents",
+    backstory="Careful and methodical",
+    max_rpm=30,
+    max_iter=12,
+    max_execution_time=180,
+)
+```
+
+## 2) تطبيق أقل امتياز على الأدوات
+
+تجنب منح كل أداة لكل Agent. امنح كل Agent فقط الأدوات المطلوبة لمهمته.
+
+### لماذا يهم ذلك
+
+- يقلل نطاق تأثير prompt injection أو أخطاء المنطق
+- يمنع الوصول العرضي إلى أنظمة غير ذات صلة
+- يحسّن إمكانية تتبع من يمكنه فعل ماذا
+
+```python
+from crewai import Agent
+from crewai.tools import FileReadTool, SerperDevTool
+
+researcher = Agent(
+    role="Researcher",
+    goal="Collect external facts",
+    backstory="Finds reliable sources",
+    tools=[SerperDevTool()],
+)
+
+auditor = Agent(
+    role="Document Auditor",
+    goal="Review internal policy documents",
+    backstory="Checks compliance language",
+    tools=[FileReadTool()],
+)
+```
+
+## 3) التعامل مع التفويض كحدود ثقة
+
+عندما يكون `allow_delegation=True`، يمكن للـ Agent توجيه العمل إلى Agents أخرى. قد يكون ذلك مفيدًا، لكنه أيضًا حد أمني.
+
+### أنماط تفويض آمنة
+
+- اجعل التفويض معطّلًا افتراضيًا
+- فعّله فقط للأدوار التي تحتاج فعلًا إلى orchestration
+- ادمجه مع قيود Task واضحة وتنفيذ محدود
+
+```python
+from crewai import Agent
+
+coordinator = Agent(
+    role="Coordinator",
+    goal="Route specialized tasks",
+    backstory="Delegates carefully",
+    allow_delegation=True,
+    max_iter=8,
+)
+```
+
+## 4) تقييد المخرجات بالمخططات والتوقعات
+
+استخدم مخرجات منظمة whenever possible لتقليل الاستجابات الغامضة أو غير الآمنة.
+
+### الضوابط الموصى بها
+
+- `output_pydantic` لمخرجات Task validated بالمخطط
+- `expected_output` لوصف معايير قبول صارمة
+
+```python
+from pydantic import BaseModel
+from crewai import Task
+
+class RiskSummary(BaseModel):
+    severity: str
+    findings: list[str]
+    recommendation: str
+
+security_task = Task(
+    description="Review tool configuration for least privilege",
+    expected_output="A structured risk summary with severity, findings, and recommendation.",
+    output_pydantic=RiskSummary,
+)
+```
+
+## 5) إضافة إشراف بشري للإجراءات عالية المخاطر
+
+للعمليات الحساسة (مثل الإجراءات المالية، تغييرات الإنتاج، أو التغييرات المؤثرة على العملاء)، أضف مراجعة بشرية في النقطة الصحيحة من مسار التنفيذ.
+
+### ماذا يفعل `human_input=True`
+
+`human_input=True` على Task يوقف التنفيذ **بعد** أن يشغّل الـ Agent أدواته وينتج نتيجة. يطلب ملاحظات بشرية على الإجابة النهائية **قبل قبول هذه المخرجات واعتمادها**. **لا** يمنع تنفيذ الأدوات — يمكن للـ Agent في Task مع `human_input=True` أن يستدعي أدوات مدمرة أو ذات آثار جانبية قبل أن يرى أي إنسان التشغيل.
+
+استخدم `human_input=True` عندما تريد أن يراجع إنسان مخرجات Task أو يعدّلها أو يوافق عليها قبل أن تصبح النتيجة الرسمية (مثل سير عمل التدريب أو مراجعة الجودة).
+
+```python
+from crewai import Task
+
+review_task = Task(
+    description="Draft the incident summary from collected logs",
+    expected_output="A concise incident summary",
+    human_input=True,
+)
+```
+
+### عندما تحتاج موافقة قبل تشغيل الأدوات
+
+لنقاط مثل:
+
+- قبل تشغيل أدوات لا رجعة فيها
+- قبل الآثار الجانبية الخارجية (بريد، تذاكر، كتابة)
+- قبل استثناءات السياسة/الأمان
+
+استخدم بوابات ما قبل التنفيذ:
+
+- **[Tool hooks](/ar/learn/tool-hooks)** مع `@on(InterceptionPoint.PRE_TOOL_CALL)` و`request_human_input()` — يمنع استدعاء الأداة حتى الموافقة
+- **[Execution hooks](/ar/learn/execution-hooks)** في تشغيل Crew وFlow
+- **[@human_feedback](/ar/learn/human-feedback-in-flows)** على خطوات Flow للموافقة على مستوى سير العمل
+
+```python
+from crewai.hooks import HookAborted, InterceptionPoint, on
+
+@on(InterceptionPoint.PRE_TOOL_CALL, tools=["send_email", "delete_file"])
+def require_approval(ctx):
+    response = ctx.request_human_input(
+        prompt=f"Approve {ctx.tool_name}?",
+        default_message=f"Args: {ctx.tool_input}\nType 'yes' to approve:",
+    )
+    if response.strip().lower() != "yes":
+        raise HookAborted(reason="denied by operator", source="approval-gate")
+```
+
+للمراجعة لاحقًا، فكّر في تفعيل `verbose=True` على Agents في تدفقات حساسة لتسهيل الفحص أثناء التصحيح ومراجعة الحوادث.
+
+## قائمة تحقق تشغيلية
+
+استخدم هذه القائمة السريعة قبل النشر في الإنتاج:
+
+- [ ] كل Agent له تنفيذ محدود (`max_rpm`، `max_iter`، `max_execution_time`)
+- [ ] الوصول إلى الأدوات محدود حسب الدور (بدون قائمة أدوات مشتركة واسعة)
+- [ ] التفويض معطّل ما لم يكن مطلوبًا صراحة
+- [ ] Tasks عالية التأثير تستخدم `output_pydantic` و`expected_output` دقيق
+- [ ] بوابات موافقة ما قبل التنفيذ موجودة للأدوات غير القابلة للتراجع أو ذات الآثار الجانبية (tool hooks أو Flow hooks أو `@human_feedback`)
+- [ ] `human_input=True` يُستخدم فقط حيث تكفي مراجعة المخرجات بعد التشغيل
+- [ ] تشغيلات Agent مسجّلة أو متتبعة للمراجعة بعد الحادث
+
+## موارد ذات صلة
+
+- [Agents](/ar/concepts/agents)
+- [Tasks](/ar/concepts/tasks)
+- [Flows](/ar/concepts/flows)
+- [Human input on execution](/ar/learn/human-input-on-execution)
+- [Human-in-the-loop](/ar/learn/human-in-the-loop)
+- [Tool Hooks](/ar/learn/tool-hooks)
+- [Tracing and observability](/ar/observability/overview)

docs/edge/en/guides/advanced/security-best-practices.mdx

@@ -0,0 +1,181 @@
+---
+title: Security Best Practices for CrewAI Agents
+description: Practical guidance for configuring CrewAI agents and crews safely in production.
+icon: shield
+---
+
+## Overview
+
+This guide focuses on **CrewAI-native controls** you can use to reduce security risk in production systems.
+
+The goal is simple: keep agent behavior bounded, least-privileged, and reviewable.
+
+## 1) Bound execution to prevent runaway behavior
+
+Use execution limits on agents and crews so failures degrade predictably instead of spiraling.
+
+### Recommended controls
+
+- `max_rpm`: cap request rate to providers
+- `max_iter`: cap iterative reasoning/tool cycles
+- `max_execution_time`: hard timeout for long-running work
+
+```python
+from crewai import Agent
+
+analyst = Agent(
+    role="Security Analyst",
+    goal="Investigate and summarize incidents",
+    backstory="Careful and methodical",
+    max_rpm=30,
+    max_iter=12,
+    max_execution_time=180,
+)
+```
+
+## 2) Apply least privilege to tools
+
+Avoid giving every tool to every agent. Give each agent only the tools required for its task.
+
+### Why this matters
+
+- Reduces blast radius for prompt injection or logic errors
+- Prevents accidental access to unrelated systems
+- Improves traceability of who can do what
+
+```python
+from crewai import Agent
+from crewai.tools import FileReadTool, SerperDevTool
+
+researcher = Agent(
+    role="Researcher",
+    goal="Collect external facts",
+    backstory="Finds reliable sources",
+    tools=[SerperDevTool()],
+)
+
+auditor = Agent(
+    role="Document Auditor",
+    goal="Review internal policy documents",
+    backstory="Checks compliance language",
+    tools=[FileReadTool()],
+)
+```
+
+## 3) Treat delegation as a trust boundary
+
+When `allow_delegation=True`, an agent can route work to other agents. That can be useful, but it is also a security boundary.
+
+### Safe delegation patterns
+
+- Keep delegation disabled by default
+- Enable it only for roles that truly need orchestration
+- Pair delegation with clear task constraints and bounded execution
+
+```python
+from crewai import Agent
+
+coordinator = Agent(
+    role="Coordinator",
+    goal="Route specialized tasks",
+    backstory="Delegates carefully",
+    allow_delegation=True,
+    max_iter=8,
+)
+```
+
+## 4) Constrain outputs with schemas and expectations
+
+Use structured outputs whenever possible to reduce ambiguous or unsafe free-form responses.
+
+### Recommended controls
+
+- `output_pydantic` for schema-validated task output
+- `expected_output` to describe strict acceptance criteria
+
+```python
+from pydantic import BaseModel
+from crewai import Task
+
+class RiskSummary(BaseModel):
+    severity: str
+    findings: list[str]
+    recommendation: str
+
+security_task = Task(
+    description="Review tool configuration for least privilege",
+    expected_output="A structured risk summary with severity, findings, and recommendation.",
+    output_pydantic=RiskSummary,
+)
+```
+
+## 5) Add human oversight for high-stakes actions
+
+For sensitive operations (for example financial actions, production mutations, or customer-impacting changes), add human review at the right point in the execution path.
+
+### What `human_input=True` does
+
+Task `human_input=True` pauses **after** the agent has run its tools and produced a result. It prompts for human feedback on the final answer **before that output is accepted and finalized**. It does **not** gate tool execution — an agent on a task with `human_input=True` can still call destructive or side-effect tools before any human sees the run.
+
+Use `human_input=True` when you want a human to review, refine, or approve the task output before it becomes the official result (for example training workflows or quality review).
+
+```python
+from crewai import Task
+
+review_task = Task(
+    description="Draft the incident summary from collected logs",
+    expected_output="A concise incident summary",
+    human_input=True,
+)
+```
+
+### When you need approval before tools run
+
+For checkpoints such as:
+
+- Before running irreversible tools
+- Before external side effects (emails, tickets, writes)
+- Before policy or security exceptions
+
+Use pre-execution gates instead:
+
+- **[Tool hooks](/en/learn/tool-hooks)** with `@on(InterceptionPoint.PRE_TOOL_CALL)` and `request_human_input()` — blocks the tool call until approved
+- **[Execution hooks](/en/learn/execution-hooks)** on Crew and Flow runs
+- **[@human_feedback](/en/learn/human-feedback-in-flows)** on Flow steps for workflow-level approval
+
+```python
+from crewai.hooks import HookAborted, InterceptionPoint, on
+
+@on(InterceptionPoint.PRE_TOOL_CALL, tools=["send_email", "delete_file"])
+def require_approval(ctx):
+    response = ctx.request_human_input(
+        prompt=f"Approve {ctx.tool_name}?",
+        default_message=f"Args: {ctx.tool_input}\nType 'yes' to approve:",
+    )
+    if response.strip().lower() != "yes":
+        raise HookAborted(reason="denied by operator", source="approval-gate")
+```
+
+For reviewability after the fact, consider enabling `verbose=True` on agents involved in sensitive flows so execution details are easier to inspect during debugging and incident review.
+
+## Operational checklist
+
+Use this quick checklist before production rollout:
+
+- [ ] Every agent has bounded execution (`max_rpm`, `max_iter`, `max_execution_time`)
+- [ ] Tool access is scoped per role (no broad shared tool list)
+- [ ] Delegation is disabled unless explicitly required
+- [ ] High-impact tasks use `output_pydantic` and precise `expected_output`
+- [ ] Pre-execution approval gates exist for irreversible or side-effect tools (tool hooks, Flow hooks, or `@human_feedback`)
+- [ ] `human_input=True` is used only where post-run output review is sufficient
+- [ ] Agent runs are logged or traced for post-incident review
+
+## Related resources
+
+- [Agents](/en/concepts/agents)
+- [Tasks](/en/concepts/tasks)
+- [Flows](/en/concepts/flows)
+- [Human input on execution](/en/learn/human-input-on-execution)
+- [Human-in-the-loop](/en/learn/human-in-the-loop)
+- [Tool Hooks](/en/learn/tool-hooks)
+- [Tracing and observability](/en/observability/overview)

docs/edge/ko/guides/advanced/security-best-practices.mdx

@@ -0,0 +1,181 @@
+---
+title: CrewAI Agent 보안 모범 사례
+description: 프로덕션에서 CrewAI Agent와 Crew를 안전하게 구성하기 위한 실용 가이드.
+icon: shield
+---
+
+## 개요
+
+이 가이드는 프로덕션 시스템에서 보안 위험을 줄이기 위해 사용할 수 있는 **CrewAI 기본 제어**에 초점을 맞춥니다.
+
+목표는 간단합니다. Agent 동작을 제한하고, 최소 권한을 적용하며, 검토 가능하게 유지하는 것입니다.
+
+## 1) 실행 범위를 제한해 폭주 동작 방지
+
+Agent와 Crew에 실행 제한을 설정하면 실패가 확산되기보다 예측 가능하게 처리됩니다.
+
+### 권장 제어
+
+- `max_rpm`: 프로바이더 요청 속도 상한
+- `max_iter`: 반복 추론/도구 호출 사이클 상한
+- `max_execution_time`: 장시간 작업에 대한 하드 타임아웃
+
+```python
+from crewai import Agent
+
+analyst = Agent(
+    role="Security Analyst",
+    goal="Investigate and summarize incidents",
+    backstory="Careful and methodical",
+    max_rpm=30,
+    max_iter=12,
+    max_execution_time=180,
+)
+```
+
+## 2) 도구에 최소 권한 적용
+
+모든 Agent에게 모든 도구를 주지 마세요. 각 Agent에는 해당 Task에 필요한 도구만 제공하세요.
+
+### 중요한 이유
+
+- 프롬프트 인젝션이나 로직 오류의 영향 범위 축소
+- 관련 없는 시스템에 대한 우발적 접근 방지
+- 누가 무엇을 할 수 있는지 추적성 향상
+
+```python
+from crewai import Agent
+from crewai.tools import FileReadTool, SerperDevTool
+
+researcher = Agent(
+    role="Researcher",
+    goal="Collect external facts",
+    backstory="Finds reliable sources",
+    tools=[SerperDevTool()],
+)
+
+auditor = Agent(
+    role="Document Auditor",
+    goal="Review internal policy documents",
+    backstory="Checks compliance language",
+    tools=[FileReadTool()],
+)
+```
+
+## 3) 위임을 신뢰 경계로 취급
+
+`allow_delegation=True`이면 Agent가 다른 Agent에게 작업을 라우팅할 수 있습니다. 유용할 수 있지만 보안 경계이기도 합니다.
+
+### 안전한 위임 패턴
+
+- 기본값으로 위임 비활성화
+- 실제로 오케스트레이션이 필요한 역할에만 활성화
+- 명확한 Task 제약과 제한된 실행과 함께 사용
+
+```python
+from crewai import Agent
+
+coordinator = Agent(
+    role="Coordinator",
+    goal="Route specialized tasks",
+    backstory="Delegates carefully",
+    allow_delegation=True,
+    max_iter=8,
+)
+```
+
+## 4) 스키마와 기대 출력으로 출력 제한
+
+가능한 한 구조화된 출력을 사용해 모호하거나 위험한 자유 형식 응답을 줄이세요.
+
+### 권장 제어
+
+- `output_pydantic`: 스키마로 검증되는 Task 출력
+- `expected_output`: 엄격한 수용 기준 설명
+
+```python
+from pydantic import BaseModel
+from crewai import Task
+
+class RiskSummary(BaseModel):
+    severity: str
+    findings: list[str]
+    recommendation: str
+
+security_task = Task(
+    description="Review tool configuration for least privilege",
+    expected_output="A structured risk summary with severity, findings, and recommendation.",
+    output_pydantic=RiskSummary,
+)
+```
+
+## 5) 고위험 작업에 대한 인간 감독 추가
+
+민감한 작업(예: 금융 작업, 프로덕션 변경, 고객 영향 변경)에는 실행 경로의 올바른 지점에서 인간 검토를 추가하세요.
+
+### `human_input=True`가 하는 일
+
+Task의 `human_input=True`는 Agent가 도구를 실행하고 결과를 생성한 **후**에 일시 중지합니다. 최종 답변에 대한 인간 피드백을 요청하여 **해당 출력이 수락·확정되기 전**에 검토할 수 있게 합니다. 도구 실행을 차단하지 **않습니다** — `human_input=True`인 Task의 Agent도 사람이 실행을 보기 전에 파괴적이거나 부수 효과가 있는 도구를 호출할 수 있습니다.
+
+공식 결과가 되기 전에 Task 출력을 검토·수정·승인하려는 경우(예: 트레이닝 워크플로, 품질 검토)에 `human_input=True`를 사용하세요.
+
+```python
+from crewai import Task
+
+review_task = Task(
+    description="Draft the incident summary from collected logs",
+    expected_output="A concise incident summary",
+    human_input=True,
+)
+```
+
+### 도구 실행 전 승인이 필요한 경우
+
+다음과 같은 체크포인트에는 사전 실행 게이트를 사용하세요.
+
+- 되돌릴 수 없는 도구 실행 전
+- 외부 부수 효과(이메일, 티켓, 쓰기) 전
+- 정책/보안 예외 전
+
+사전 실행 게이트 예:
+
+- **`@on(InterceptionPoint.PRE_TOOL_CALL)`** 및 `request_human_input()`이 있는 **[Tool hooks](/ko/learn/tool-hooks)** — 승인 전까지 도구 호출 차단
+- Crew 및 Flow 실행의 **[Execution hooks](/ko/learn/execution-hooks)**
+- 워크플로 수준 승인을 위한 Flow 단계의 **[@human_feedback](/ko/learn/human-feedback-in-flows)**
+
+```python
+from crewai.hooks import HookAborted, InterceptionPoint, on
+
+@on(InterceptionPoint.PRE_TOOL_CALL, tools=["send_email", "delete_file"])
+def require_approval(ctx):
+    response = ctx.request_human_input(
+        prompt=f"Approve {ctx.tool_name}?",
+        default_message=f"Args: {ctx.tool_input}\nType 'yes' to approve:",
+    )
+    if response.strip().lower() != "yes":
+        raise HookAborted(reason="denied by operator", source="approval-gate")
+```
+
+사후 검토를 위해 민감한 Flow에 관여하는 Agent에 `verbose=True`를 설정하면 디버깅 및 사후 분석 시 실행 세부 정보를 더 쉽게 확인할 수 있습니다.
+
+## 운영 체크리스트
+
+프로덕션 배포 전 빠른 체크리스트:
+
+- [ ] 모든 Agent에 실행 제한(`max_rpm`, `max_iter`, `max_execution_time`) 적용
+- [ ] 역할별로 도구 접근 범위 제한(광범위한 공유 도구 목록 없음)
+- [ ] 명시적으로 필요한 경우가 아니면 위임 비활성화
+- [ ] 고영향 Task에 `output_pydantic`과 정확한 `expected_output` 사용
+- [ ] 되돌릴 수 없거나 부수 효과가 있는 도구에 사전 실행 승인 게이트 존재(tool hooks, Flow hooks, `@human_feedback`)
+- [ ] `human_input=True`는 실행 후 출력 검토로 충분한 경우에만 사용
+- [ ] Agent 실행이 사후 검토를 위해 로깅 또는 추적됨
+
+## 관련 리소스
+
+- [Agents](/ko/concepts/agents)
+- [Tasks](/ko/concepts/tasks)
+- [Flows](/ko/concepts/flows)
+- [Human input on execution](/ko/learn/human-input-on-execution)
+- [Human-in-the-loop](/ko/learn/human-in-the-loop)
+- [Tool Hooks](/ko/learn/tool-hooks)
+- [Tracing and observability](/ko/observability/overview)

docs/edge/pt-BR/guides/advanced/security-best-practices.mdx

@@ -0,0 +1,181 @@
+---
+title: Melhores Práticas de Segurança para Agentes CrewAI
+description: Orientação prática para configurar agentes e crews CrewAI com segurança em produção.
+icon: shield
+---
+
+## Visão geral
+
+Este guia foca em **controles nativos do CrewAI** que você pode usar para reduzir riscos de segurança em sistemas de produção.
+
+O objetivo é simples: manter o comportamento do agente limitado, com privilégio mínimo e auditável.
+
+## 1) Limitar a execução para evitar comportamento descontrolado
+
+Use limites de execução em agentes e crews para que falhas degradem de forma previsível em vez de escalar.
+
+### Controles recomendados
+
+- `max_rpm`: limitar taxa de requisições aos provedores
+- `max_iter`: limitar ciclos iterativos de raciocínio/ferramentas
+- `max_execution_time`: timeout rígido para trabalhos longos
+
+```python
+from crewai import Agent
+
+analyst = Agent(
+    role="Security Analyst",
+    goal="Investigate and summarize incidents",
+    backstory="Careful and methodical",
+    max_rpm=30,
+    max_iter=12,
+    max_execution_time=180,
+)
+```
+
+## 2) Aplicar privilégio mínimo às ferramentas
+
+Evite dar todas as ferramentas a todos os agentes. Dê a cada agente apenas as ferramentas necessárias para sua tarefa.
+
+### Por que isso importa
+
+- Reduz o raio de impacto de prompt injection ou erros de lógica
+- Evita acesso acidental a sistemas não relacionados
+- Melhora a rastreabilidade de quem pode fazer o quê
+
+```python
+from crewai import Agent
+from crewai.tools import FileReadTool, SerperDevTool
+
+researcher = Agent(
+    role="Researcher",
+    goal="Collect external facts",
+    backstory="Finds reliable sources",
+    tools=[SerperDevTool()],
+)
+
+auditor = Agent(
+    role="Document Auditor",
+    goal="Review internal policy documents",
+    backstory="Checks compliance language",
+    tools=[FileReadTool()],
+)
+```
+
+## 3) Tratar delegação como um limite de confiança
+
+Quando `allow_delegation=True`, um agente pode encaminhar trabalho a outros agentes. Isso pode ser útil, mas também é um limite de segurança.
+
+### Padrões seguros de delegação
+
+- Mantenha a delegação desativada por padrão
+- Ative apenas para papéis que realmente precisam de orquestração
+- Combine delegação com restrições claras de tarefa e execução limitada
+
+```python
+from crewai import Agent
+
+coordinator = Agent(
+    role="Coordinator",
+    goal="Route specialized tasks",
+    backstory="Delegates carefully",
+    allow_delegation=True,
+    max_iter=8,
+)
+```
+
+## 4) Restringir saídas com schemas e expectativas
+
+Use saídas estruturadas sempre que possível para reduzir respostas ambíguas ou inseguras em texto livre.
+
+### Controles recomendados
+
+- `output_pydantic` para saída de Task validada por schema
+- `expected_output` para descrever critérios rígidos de aceitação
+
+```python
+from pydantic import BaseModel
+from crewai import Task
+
+class RiskSummary(BaseModel):
+    severity: str
+    findings: list[str]
+    recommendation: str
+
+security_task = Task(
+    description="Review tool configuration for least privilege",
+    expected_output="A structured risk summary with severity, findings, and recommendation.",
+    output_pydantic=RiskSummary,
+)
+```
+
+## 5) Adicionar supervisão humana para ações de alto impacto
+
+Para operações sensíveis (por exemplo ações financeiras, mutações em produção ou mudanças que afetam clientes), adicione revisão humana no ponto certo do caminho de execução.
+
+### O que `human_input=True` faz
+
+`human_input=True` em uma Task pausa **depois** que o agente executou suas ferramentas e produziu um resultado. Ele solicita feedback humano sobre a resposta final **antes que essa saída seja aceita e finalizada**. **Não** bloqueia a execução de ferramentas — um agente em uma Task com `human_input=True` ainda pode chamar ferramentas destrutivas ou com efeitos colaterais antes que qualquer humano veja a execução.
+
+Use `human_input=True` quando quiser que um humano revise, refine ou aprove a saída da Task antes que ela se torne o resultado oficial (por exemplo fluxos de treinamento ou revisão de qualidade).
+
+```python
+from crewai import Task
+
+review_task = Task(
+    description="Draft the incident summary from collected logs",
+    expected_output="A concise incident summary",
+    human_input=True,
+)
+```
+
+### Quando você precisa de aprovação antes das ferramentas executarem
+
+Para checkpoints como:
+
+- Antes de executar ferramentas irreversíveis
+- Antes de efeitos colaterais externos (e-mails, tickets, gravações)
+- Antes de exceções de política ou segurança
+
+Use gates de pré-execução:
+
+- **[Tool hooks](/pt-BR/learn/tool-hooks)** com `@on(InterceptionPoint.PRE_TOOL_CALL)` e `request_human_input()` — bloqueia a chamada da ferramenta até aprovação
+- **[Execution hooks](/pt-BR/learn/execution-hooks)** em execuções de Crew e Flow
+- **[@human_feedback](/pt-BR/learn/human-feedback-in-flows)** em passos de Flow para aprovação no nível do workflow
+
+```python
+from crewai.hooks import HookAborted, InterceptionPoint, on
+
+@on(InterceptionPoint.PRE_TOOL_CALL, tools=["send_email", "delete_file"])
+def require_approval(ctx):
+    response = ctx.request_human_input(
+        prompt=f"Approve {ctx.tool_name}?",
+        default_message=f"Args: {ctx.tool_input}\nType 'yes' to approve:",
+    )
+    if response.strip().lower() != "yes":
+        raise HookAborted(reason="denied by operator", source="approval-gate")
+```
+
+Para auditabilidade posterior, considere habilitar `verbose=True` em agentes envolvidos em fluxos sensíveis para facilitar a inspeção durante depuração e revisão de incidentes.
+
+## Checklist operacional
+
+Use este checklist rápido antes do rollout em produção:
+
+- [ ] Cada agente tem execução limitada (`max_rpm`, `max_iter`, `max_execution_time`)
+- [ ] O acesso a ferramentas é escopado por papel (sem lista ampla compartilhada)
+- [ ] A delegação está desativada, salvo quando explicitamente necessária
+- [ ] Tasks de alto impacto usam `output_pydantic` e `expected_output` preciso
+- [ ] Gates de aprovação pré-execução existem para ferramentas irreversíveis ou com efeito colateral (tool hooks, Flow hooks ou `@human_feedback`)
+- [ ] `human_input=True` é usado apenas onde revisão pós-execução da saída é suficiente
+- [ ] Execuções de agentes são registradas ou rastreadas para revisão pós-incidente
+
+## Recursos relacionados
+
+- [Agents](/pt-BR/concepts/agents)
+- [Tasks](/pt-BR/concepts/tasks)
+- [Flows](/pt-BR/concepts/flows)
+- [Human input on execution](/pt-BR/learn/human-input-on-execution)
+- [Human-in-the-loop](/pt-BR/learn/human-in-the-loop)
+- [Tool Hooks](/pt-BR/learn/tool-hooks)
+- [Tracing and observability](/pt-BR/observability/overview)