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Course Outline

AutoGen in the Enterprise Context

  • Understanding why intelligent agents are critical for business operations.
  • Overview of AutoGen’s architecture and its extensibility features.
  • Key considerations regarding security, traceability, and governance.

Enterprise Workflow Automation with AutoGen

  • Designing multi-agent workflows for effective task coordination.
  • Exploring role-based automation scenarios: handling requests, approvals, and summarization.
  • Implementing auto-execution and escalation logic to ensure business continuity.

AutoGen with LangChain Integration

  • Examining LangChain components and their compatibility with AutoGen.
  • Chaining agents and tools while managing memory, tools, and logic.
  • Utilizing the LangChain Expression Language (LCEL) for developing complex workflows.

Retrieval-Augmented Generation (RAG) Pipelines

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

Integration with Enterprise Tools

  • Leveraging APIs to connect with Jira, Slack, Outlook, SharePoint, and other platforms.
  • Triggering workflows through chat interfaces and ticketing systems.
  • Enabling real-time notifications, logging, and auditing.

Deployment, Monitoring, and Scaling

  • Packaging AutoGen agents for efficient deployment.
  • Monitoring agent interactions, usage patterns, and performance metrics.
  • Scaling agents across various departments and geographical regions.

Enterprise Use Case Prototyping Lab

  • Group ideation sessions focused on enterprise automation scenarios.
  • Building custom agent workflows with guidance from the instructor.
  • Simulating production environments for rigorous validation.

Summary and Next Steps

Requirements

  • Strong proficiency in Python programming.
  • Practical experience with Large Language Models (LLMs) and prompt engineering.
  • Familiarity with enterprise automation tools and workflow management.

Target Audience

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

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