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* feat: adopt directory-based docs versioning with Edge channel Switch docs.crewai.com from navigation-only versioning (every version selector entry rendered the same docs/<lang>/* source files) to Mintlify's directory-based versioning so each version selector entry renders its own snapshot. Add an "Edge" channel under docs/edge/<lang>/* that always reflects main HEAD for unreleased work, eliminating pre-release leakage onto frozen release labels. External links to canonical /<lang>/* URLs are preserved via wildcard redirects that always land on the current default version. Layout: - docs/edge/<lang>/* rolling source (you edit here) - docs/edge/enterprise-api.*.yaml - docs/v<X.Y.Z>/<lang>/* frozen, immutable snapshots - docs/v<X.Y.Z>/enterprise-api.*.yaml - docs/images/ shared, append-only - docs/docs.json nav + redirects URLs follow the Mintlify-idiomatic shape: /edge/<lang>/<page> for Edge, /v<X.Y.Z>/<lang>/<page> for every frozen snapshot. The wildcard redirects /<lang>/:slug* -> /<default>/<lang>/:slug* keep stale links working, and every freeze rewrites them (plus all per-section/per-page redirects) so destinations always resolve to the current default without depending on a second redirect hop. Release flow integration (devtools release): - New module crewai_devtools.docs_versioning.freeze() materialises docs/v<X.Y.Z>/ from docs/edge/, rewrites openapi: refs inside the snapshot, inserts the version into every language block in docs.json, and refreshes all redirect destinations. - _update_docs_and_create_pr() in cli.py now calls that freeze during Phase 2 of devtools release. Edge changelogs are updated first (so the snapshot freeze picks them up), then the snapshot is staged alongside docs.json, branched as docs/freeze-v<X.Y.Z>, and the PR is titled [docs-freeze] docs: snapshot and changelog for v<X.Y.Z> — the title prefix the new CI guard reads. - The PR still gates tag, GitHub release, PyPI publish, and the enterprise release as before; no new PRs are added. - Pre-releases (1.X.YaN, 1.X.YbN, ...) skip the snapshot — they ride Edge — and the docs PR title omits the [docs-freeze] prefix. - docs_check (AI-generated docs scaffolding) writes to docs/edge/<lang>/* so newly-generated unreleased docs land in Edge and never accidentally touch a frozen snapshot. Migration scripts (one-shot): - scripts/docs/freeze_historical_versions.py reconstructs all 16 historical snapshots (v1.10.0 .. v1.14.7) from git tags via git archive | tar, rewriting openapi: MDX refs so each snapshot reads its own enterprise-api YAML rather than the live one. - scripts/docs/prefix_version_paths.py one-shot-migrates docs.json: rewrites every page path in 16 versioned blocks to point under docs/v<X.Y.Z>/, inserts a new Edge entry per language, tags v1.14.7 as Latest (default), prunes pages whose target file doesn't exist in the snapshot (e.g. docs/ar/ didn't exist before v1.12.0), and writes the wildcard + per-section redirects. - scripts/docs/freeze_current_edge.py is now a thin CLI wrapper around docs_versioning.freeze for manual one-off freezes (e.g. retroactively snapshotting a forgotten release). CI guards (.github/workflows/docs-snapshots.yml): - Frozen snapshots under docs/v[0-9]*/ are immutable; only PRs whose title contains [docs-freeze] (i.e. release-cut PRs generated by devtools release or the manual wrapper) may modify them. - Images under docs/images/ are append-only since snapshots share a single image directory. Deleting or renaming an image breaks every historical snapshot that still references it. Restored docs/images/crewai-otel-export.png from PR #3673; it was deleted in PR #4908 but v1.10.0 / v1.10.1 snapshots still reference it. Restoring instead of editing the snapshots preserves historical rendering fidelity and validates the new append-only rule retroactively. Tests: - lib/devtools/tests/test_docs_versioning.py covers the freeze: file copy, openapi rewrite, version insertion, default demotion, redirect upserts, per-section redirect rewriting, idempotency, and invalid inputs. Verified locally with mintlify broken-links: 0 broken links across the full site (Edge + 16 frozen versions, 4 locales). AGENTS.md (repo root) is the contributor guide for the new model; RELEASING.md is the release-cut runbook; README's Contribution section links to both. Co-authored-by: Cursor <cursoragent@cursor.com> * style: resolve linter issues --------- Co-authored-by: Cursor <cursoragent@cursor.com>
189 lines
7.4 KiB
Plaintext
189 lines
7.4 KiB
Plaintext
---
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title: أداة استدعاء وكيل Bedrock
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description: تتيح لوكلاء CrewAI استدعاء وكلاء Amazon Bedrock والاستفادة من قدراتهم ضمن سير العمل الخاص بك
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icon: aws
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mode: "wide"
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---
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# `BedrockInvokeAgentTool`
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تتيح `BedrockInvokeAgentTool` لوكلاء CrewAI استدعاء وكلاء Amazon Bedrock والاستفادة من قدراتهم ضمن سير العمل الخاص بك.
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## التثبيت
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```bash
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uv pip install 'crewai[tools]'
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```
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## المتطلبات
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- بيانات اعتماد AWS مُهيأة (إما من خلال متغيرات البيئة أو AWS CLI)
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- حزمتا `boto3` و `python-dotenv`
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- الوصول إلى وكلاء Amazon Bedrock
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## الاستخدام
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إليك كيفية استخدام الأداة مع وكيل CrewAI:
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```python {2, 4-8}
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from crewai import Agent, Task, Crew
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from crewai_tools.aws.bedrock.agents.invoke_agent_tool import BedrockInvokeAgentTool
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# Initialize the tool
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agent_tool = BedrockInvokeAgentTool(
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agent_id="your-agent-id",
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agent_alias_id="your-agent-alias-id"
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)
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# Create a CrewAI agent that uses the tool
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aws_expert = Agent(
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role='AWS Service Expert',
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goal='Help users understand AWS services and quotas',
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backstory='I am an expert in AWS services and can provide detailed information about them.',
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tools=[agent_tool],
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verbose=True
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)
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# Create a task for the agent
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quota_task = Task(
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description="Find out the current service quotas for EC2 in us-west-2 and explain any recent changes.",
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agent=aws_expert
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)
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# Create a crew with the agent
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crew = Crew(
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agents=[aws_expert],
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tasks=[quota_task],
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verbose=2
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)
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# Run the crew
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result = crew.kickoff()
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print(result)
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```
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## معاملات الأداة
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| المعامل | النوع | مطلوب | الافتراضي | الوصف |
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|:---------|:-----|:---------|:--------|:------------|
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| **agent_id** | `str` | نعم | None | المعرّف الفريد لوكيل Bedrock |
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| **agent_alias_id** | `str` | نعم | None | المعرّف الفريد لاسم الوكيل المستعار |
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| **session_id** | `str` | لا | الطابع الزمني | المعرّف الفريد للجلسة |
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| **enable_trace** | `bool` | لا | False | ما إذا كان سيتم تفعيل التتبع لأغراض التصحيح |
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| **end_session** | `bool` | لا | False | ما إذا كان سيتم إنهاء الجلسة بعد الاستدعاء |
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| **description** | `str` | لا | None | وصف مخصص للأداة |
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## متغيرات البيئة
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```bash
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BEDROCK_AGENT_ID=your-agent-id # Alternative to passing agent_id
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BEDROCK_AGENT_ALIAS_ID=your-agent-alias-id # Alternative to passing agent_alias_id
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AWS_REGION=your-aws-region # Defaults to us-west-2
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AWS_ACCESS_KEY_ID=your-access-key # Required for AWS authentication
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AWS_SECRET_ACCESS_KEY=your-secret-key # Required for AWS authentication
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```
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## الاستخدام المتقدم
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### سير عمل متعدد الوكلاء مع إدارة الجلسات
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```python {2, 4-22}
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from crewai import Agent, Task, Crew, Process
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from crewai_tools.aws.bedrock.agents.invoke_agent_tool import BedrockInvokeAgentTool
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# Initialize tools with session management
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initial_tool = BedrockInvokeAgentTool(
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agent_id="your-agent-id",
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agent_alias_id="your-agent-alias-id",
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session_id="custom-session-id"
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)
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followup_tool = BedrockInvokeAgentTool(
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agent_id="your-agent-id",
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agent_alias_id="your-agent-alias-id",
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session_id="custom-session-id"
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)
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final_tool = BedrockInvokeAgentTool(
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agent_id="your-agent-id",
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agent_alias_id="your-agent-alias-id",
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session_id="custom-session-id",
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end_session=True
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)
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# Create agents for different stages
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researcher = Agent(
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role='AWS Service Researcher',
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goal='Gather information about AWS services',
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backstory='I am specialized in finding detailed AWS service information.',
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tools=[initial_tool]
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)
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analyst = Agent(
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role='Service Compatibility Analyst',
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goal='Analyze service compatibility and requirements',
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backstory='I analyze AWS services for compatibility and integration possibilities.',
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tools=[followup_tool]
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)
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summarizer = Agent(
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role='Technical Documentation Writer',
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goal='Create clear technical summaries',
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backstory='I specialize in creating clear, concise technical documentation.',
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tools=[final_tool]
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)
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# Create tasks
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research_task = Task(
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description="Find all available AWS services in us-west-2 region.",
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agent=researcher
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)
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analysis_task = Task(
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description="Analyze which services support IPv6 and their implementation requirements.",
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agent=analyst
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)
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summary_task = Task(
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description="Create a summary of IPv6-compatible services and their key features.",
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agent=summarizer
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)
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# Create a crew with the agents and tasks
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crew = Crew(
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agents=[researcher, analyst, summarizer],
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tasks=[research_task, analysis_task, summary_task],
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process=Process.sequential,
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verbose=2
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)
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# Run the crew
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result = crew.kickoff()
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```
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## حالات الاستخدام
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### التعاون الهجين متعدد الوكلاء
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- إنشاء سير عمل حيث يتعاون وكلاء CrewAI مع وكلاء Bedrock المُدارة التي تعمل كخدمات في AWS
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- تمكين سيناريوهات حيث تتم معالجة البيانات الحساسة داخل بيئة AWS الخاصة بك بينما تعمل وكلاء أخرى خارجياً
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- ربط وكلاء CrewAI المحلية مع وكلاء Bedrock السحابية لسير عمل ذكاء موزع
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### سيادة البيانات والامتثال
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- الحفاظ على سير عمل الوكلاء الحساسة للبيانات داخل بيئة AWS الخاصة بك مع السماح لوكلاء CrewAI الخارجية بتنسيق المهام
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- الحفاظ على الامتثال لمتطلبات إقامة البيانات من خلال معالجة المعلومات الحساسة فقط داخل حساب AWS الخاص بك
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- تمكين التعاون الآمن متعدد الوكلاء حيث لا يمكن لبعض الوكلاء الوصول إلى البيانات الخاصة بمؤسستك
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### التكامل السلس مع خدمات AWS
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- الوصول إلى أي خدمة AWS من خلال Amazon Bedrock Actions دون كتابة كود تكامل معقد
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- تمكين وكلاء CrewAI من التفاعل مع خدمات AWS من خلال طلبات اللغة الطبيعية
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- الاستفادة من قدرات وكلاء Bedrock المبنية مسبقاً للتفاعل مع خدمات AWS مثل Bedrock Knowledge Bases و Lambda والمزيد
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### هياكل وكلاء هجينة قابلة للتوسع
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- تفريغ المهام الحسابية المكثفة إلى وكلاء Bedrock المُدارة بينما تعمل المهام الخفيفة في CrewAI
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- توسيع معالجة الوكلاء من خلال توزيع أعباء العمل بين وكلاء CrewAI المحلية ووكلاء Bedrock السحابية
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### التعاون بين المؤسسات
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- تمكين التعاون الآمن بين وكلاء CrewAI الخاصة بمؤسستك ووكلاء Bedrock الخاصة بالمؤسسات الشريكة
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- إنشاء سير عمل حيث يمكن دمج الخبرة الخارجية من وكلاء Bedrock دون كشف البيانات الحساسة
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- بناء أنظمة وكلاء تمتد عبر حدود المؤسسات مع الحفاظ على الأمان والتحكم في البيانات
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