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Course Outline
AutoGen in the Enterprise Context
- The importance of intelligent agents for business operations.
- Review of AutoGen’s architecture and extensibility.
- Considerations regarding security, traceability, and governance.
Enterprise Workflow Automation with AutoGen
- Designing multi-agent workflows for task coordination.
- Role-based automation scenarios: request handling, approvals, and summaries.
- Auto-execution and escalation logic for business continuity.
AutoGen with LangChain Integration
- LangChain components and their compatibility with AutoGen.
- Chaining agents and tools with memory, tools, and logic.
- Using the LangChain Expression Language (LCEL) for complex workflows.
Retrieval-Augmented Generation (RAG) Pipelines
- Connecting AutoGen agents with enterprise knowledge bases.
- Embedding, vector search, and retrieval pipelines.
- Private data augmentation using open-source or proprietary models.
Integration with Enterprise Tools
- Using APIs to connect Jira, Slack, Outlook, SharePoint, and more.
- Triggering workflows via chat interfaces and ticketing systems.
- 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.
Target Audience
- Enterprise AI teams.
- Solution architects.
- Innovation strategists.
21 Hours
Testimonials (1)
I liked that he constantly provided examples but also offered time for individual work on what he presented.
