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Course Outline
AutoGen in the Enterprise Context
- Why intelligent agents matter for business operations
- Review of AutoGen’s architecture and extensibility
- Security, traceability, and governance considerations
Enterprise Workflow Automation with AutoGen
- Designing multi-agent workflows for task coordination
- Role-based automation scenarios: request handling, approvals, summaries
- Auto-execution and escalation logic for business continuity
AutoGen with LangChain Integration
- LangChain components and compatibility with AutoGen
- Chaining agents and tools with memory, tools, and logic
- 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 with 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: 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 with LLMs and prompt engineering
- Familiarity with enterprise automation or workflow tools
Audience
- Enterprise AI teams
- Solution architects
- Innovation strategists
21 Hours
Testimonials (1)
Trainer responding to questions on the fly.
