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Duration 21 hours (3 days)
Course Outline
Understanding AutoGen in the Enterprise Environment
- The strategic importance of intelligent agents in modern business operations.
- An overview of AutoGen’s architectural design and extensibility features.
- Key considerations regarding security, traceability, and governance.
Automating Enterprise Workflows with AutoGen
- Structuring multi-agent workflows to facilitate effective task coordination.
- Implementing role-based automation scenarios, including request processing, approval chains, and summary generation.
- Defining auto-execution and escalation logic to ensure business continuity.
Integrating AutoGen with LangChain
- Exploring LangChain components and their compatibility with the AutoGen framework.
- Orchestrating agents and tools through memory, function calls, and logical flows.
- Utilizing LangChain Expression Language (LCEL) to manage complex workflows.
Building Retrieval-Augmented Generation (RAG) Pipelines
- Linking AutoGen agents to internal enterprise knowledge bases.
- Designing embedding, vector search, and retrieval mechanisms.
- Enhancing private data utilization with both open-source and proprietary models.
Connecting to Enterprise Tools
- Leveraging APIs to integrate with platforms like Jira, Slack, Outlook, and SharePoint.
- Initiating workflows through chat interfaces and ticketing systems.
- Implementing real-time notifications, comprehensive logging, and auditing capabilities.
Deployment, Monitoring, and Scaling Strategies
- Preparing and packaging AutoGen agents for stable deployment.
- Tracking agent interactions, usage metrics, and overall performance.
- Scaling agent operations across different departments and geographic regions.
Prototyping Lab for Enterprise Use Cases
- Collaborative group ideation to identify suitable enterprise automation scenarios.
- Developing custom agent workflows with direct instructor guidance.
- Simulating production conditions to validate solution robustness.
Course Summary and Recommended Next Steps
Requirements
- Strong proficiency in Python programming.
- Practical experience with Large Language Models (LLMs) and prompt engineering techniques.
- Familiarity with enterprise automation tools or workflow management software.
Target Audience
- Enterprise AI development teams.
- Solution architects.
- Innovation strategists.
Testimonials (1)
I liked that he constantly provided examples but also offered time for individual work on what he presented.
