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Beginnt 5 June 2026 09:06

Endet 5 June 2026

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Design and Build Powerful LLM Agents - Valentina Alto

Unlock the potential of LLM agents: explore their design, construction, and evolution from single to multi-agent systems. Gain insights into GenAI workflows and key components of AI agents.
Open Data Science via YouTube

Open Data Science

6076 Kurse


31 minutes

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Übersicht

Unlock the potential of LLM agents:

explore their design, construction, and evolution from single to multi-agent systems. Gain insights into GenAI workflows and key components of AI agents.

Lehrplan

  • Introduction to LLM Agents
  • Definition and history of LLM agents
    Overview of current applications
    The future landscape of LLM agents
  • Fundamentals of AI Agents
  • Core concepts and terminology
    Types of AI agents
    Key components of AI architecture
  • Designing LLM Agents
  • Principles of effective LLM agent design
    Human-centric design considerations
    Ethical guidelines and practices
  • Constructing Single-Agent Systems
  • Frameworks and tools for building LLM agents
    Integration of language models into applications
    Testing and evaluation of single-agent systems
  • Evolvement to Multi-Agent Systems
  • Benefits of multi-agent approaches
    Coordination and communication among agents
    Scalability and resource management
  • Advanced GenAI Workflows
  • Workflow automation in LLM deployments
    Continuous learning and adaptation
    Monitoring and maintenance strategies
  • Case Studies and Practical Applications
  • Real-world applications of LLM agents
    Lessons learned from industry examples
    Emerging trends and innovations
  • Project: Design and Build Your Own LLM Agent
  • Step-by-step guide
    Tools and resources
    Presentation and feedback session
  • Future Directions and Research Opportunities
  • Open research questions
    Potential interdisciplinary collaborations
    Participating in the AI community and staying updated on developments
  • Course Review and Q&A Session
  • Recap of key learnings
    Addressing student queries and feedback
    Course certification and acknowledgment

Fachgebiete

Data Science