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Course Outline

AutoGen in the Enterprise Context

  • Understanding why intelligent agents are critical for business operations
  • Reviewing AutoGen’s architecture and extensibility
  • Addressing security, traceability, and governance considerations

Enterprise Workflow Automation with AutoGen

  • Designing multi-agent workflows to coordinate tasks
  • Implementing role-based automation scenarios: request handling, approvals, and summaries
  • Establishing auto-execution and escalation logic to ensure business continuity

AutoGen with LangChain Integration

  • Exploring LangChain components and their compatibility with AutoGen
  • Chaining agents and tools with memory, tools, and logic
  • Utilizing LangChain Expression Language (LCEL) for complex workflows

Retrieval-Augmented Generation (RAG) Pipelines

  • Connecting AutoGen agents with enterprise knowledge bases
  • Implementing embedding, vector search, and retrieval pipelines
  • Augmenting private data using open-source or proprietary models

Integration with Enterprise Tools

  • Using APIs to connect Jira, Slack, Outlook, SharePoint, and other systems
  • Triggering workflows via chat interfaces and ticketing systems
  • Managing real-time notifications, logging, and auditing

Deployment, Monitoring, and Scaling

  • Packaging AutoGen agents for deployment
  • Monitoring agent interactions, usage, and performance
  • Scaling agents across departments and geographies

Enterprise Use Case Prototyping Lab

  • Group ideation: identifying enterprise scenarios for automation
  • Building custom agent workflows with instructor support
  • Simulating production environments for validation

Summary and Next Steps

Requirements

  • Proficiency in Python programming
  • Experience working with Large Language Models (LLMs) and prompt engineering
  • Familiarity with enterprise automation or workflow tools

Audience

  • Enterprise AI teams
  • Solution architects
  • Innovation strategists
 21 Hours

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