الرئيسية/الدورات/Microsoft Copilot والذكاء الاصطناعي في Azure/AI-500T00: Design and implement multi-agent AI solutions
Microsoft Copilot والذكاء الاصطناعي في AzureAI-500T00: Design and implement multi-agent AI solutions
صمّم أنظمة ذكاء اصطناعي متعددة الوكلاء وابنِها باستخدام Microsoft Foundry. خلال أربعة أيام تنشئ حلقات وكيلية تحتفظ بالحالة
4 أياممتوسط17 وحدةAI-500T00
نظرة عامة
صمّم أنظمة ذكاء اصطناعي متعددة الوكلاء وابنِها باستخدام Microsoft Foundry. خلال أربعة أيام تنشئ حلقات وكيلية تحتفظ بالحالة، وتطبّق أنماط تنسيق متقدّمة، وتربط الأدوات والبيانات، وتقيّم الوكلاء وتؤمّنهم وتشغّلهم على نطاق واسع.
المنهاج
المحتوى التقني أدناه معروضٌ باللغة الإنجليزية كما ورد في منهاج الدورة، حفاظاً على دقّة المصطلحات.
17 وحدة
- Examine production agentic loop architecture
- Examine the Foundry responses API and agents v2 model
- Implement agent reflection and planning cycles
- Design session state and context management
- Implement fork-based sessions and conversation resumption
- Migrate stateful agentic loops from agents v1 to agents v2
- Module assessment
- Differentiate agentic AI from multi-agent AI architectures
- Examine advanced orchestration architectures
- Implement hub-and-spoke orchestration
- Design parallel agent spawning and synchronization
- Compare orchestration frameworks
- Module assessment
- Design prompt chaining workflows
- Implement dynamic adaptive task decomposition
- Design agent handoff message schemas
- Ensure handoff reliability and context preservation
- Optimize decomposition granularity
- Module assessment
- Design A2A agent ecosystems at scale
- Implement distributed shared state management
- Design context isolation and sharing strategies
- Build conflict detection and resolution mechanisms
- Resolve conflicts and maintain audit trails
- Module assessment
- Design multiturn reasoning prompt architectures
- Implement prompt injection defenses
- Build system prompt frameworks for agent control
- Design multi-intervention guardrail architectures
- Implement prompt versioning and optimization
- Automate prompt regression and optimization
- Design fine-tuning strategy and data pipelines
- Module assessment
- Design production MCP server architecture
- Build MCP servers with error handling and fallback
- Implement tool selection and routing logic
- Govern tool dependencies and versioning
- Module assessment
- Design hybrid search architectures
- Implement reranking and context ranking
- Design dynamic knowledge source routing
- Optimize chunking and embedding strategies
- Module assessment
- Examine memory architecture patterns
- Implement semantic memory with vector storage
- Optimize memory retrieval and context injection
- Configure context window optimization
- Design memory retention and consolidation
- Enforce memory privacy and audit compliance
- Module assessment
- Design multi-agent deployment pipelines
- Implement progressive deployment strategies
- Configure multi-environment agent deployment strategies
- Automate rollback procedures
- Module assessment
- Apply zero-trust identity to agent networks
- Secure agent access with JIT and workload identity
- Design authentication flows and secrets lifecycle
- Prevent lateral movement in agent networks
- Implement tenant context propagation and data isolation
- Validate tenant boundaries and enforce encryption
- Configure compliance controls for regulated agent deployments
- Module assessment
- Design fairness and bias monitoring
- Implement transparency and explainability
- Configure privacy protection in multi-agent workflows
- Establish audit and accountability frameworks
- Module assessment
- Design agent versioning and approval workflows
- Implement usage quotas and rate limiting
- Design cost allocation and chargeback models
- Establish agent retirement and deprecation processes
- Module assessment
- Design distributed tracing for multi-agent solutions
- Implement structured logging for agent decisions
- Configure telemetry aggregation and dashboards
- Build anomaly detection for agent behavior
- Module assessment
- Define multi-agent success metrics
- Implement LLM-as-judge evaluation for multi-agent systems
- Design synthetic test datasets for multi-agent evaluation
- Build regression testing pipelines to detect agent drift
- Module assessment
- Design model routing for agent ecosystems
- Implement multi-level caching strategies
- Optimize token usage and context management
- Balance quality, cost, and latency tradeoffs
- Module assessment
- Design confidence-based escalation for human intervention
- Implement approval workflows for agent-initiated actions
- Build active learning from human feedback
- Configure audit workflows for regulated decisions
- Module assessment
- Implement agent replay for production debugging
- Design root cause analysis for agent failures
- Configure automated incident detection and remediation
- Establish incident response and post-mortem processes
- Module assessment
لمن هذه الدورة
مطوّرو الذكاء الاصطناعي ومعماريو الحلول المتمرّسون.
قبل أن تبدأ
لا متطلبات مسبقة رسمية. يكفي الفضول والاستعداد للتطبيق العملي.
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