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- Defining Agency and the Spectrum of Autonomy
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Classroom Contents
Building Agentic AI Workloads - Crash Course
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- 1 - Introduction and Speaker Background
- 2 - A Brief History of Artificial Intelligence 1940s–Present
- 3 - Traditional Machine Learning vs. Generative AI
- 4 - The Three Pillars of AI: Algorithms, Data, and Compute
- 5 - Specific Tasks vs. General Task Execution
- 6 - Defining Agency and the Spectrum of Autonomy
- 7 - Agentic Milestone Timeline 2017–2026
- 8 - What is a Generative AI Agent?
- 9 - Agents vs. Workflows: Dynamic Flow vs. Static Paths
- 10 - Pros and Cons of Agentic Systems
- 11 - Patterns and Anti-patterns: When to Use Agents
- 12 - The Core Components of an Agent
- 13 - Choosing the Right LLM for Your Agent
- 14 - Crafting Identity with System Prompts
- 15 - Understanding Memory: Intrinsic, Short-term, and Long-term
- 16 - Enhancing Capabilities with Tools and Actions
- 17 - Hands-on Implementation: From Single LLM Call to Python Agent
- 18 - Adding Memory and History to Your Custom Agent
- 19 - Building Agents with Frameworks LangChain
- 20 - The Evolving Landscape of Models and Frameworks
- 21 - Agentic Architectural Patterns: Supervisor vs. Swarm
- 22 - Case Study: Single Agent vs. Supervisor Architecture
- 23 - Deep Dive: Swarm Architecture Performance
- 24 - When to Choose Multi-agent Systems
- 25 - Interface Protocols: MCP, A2A, and AGUI
- 26 - How to Evaluate Agentic Systems LLM vs. System vs. App
- 27 - Evaluation Methods: Code-based, LLM-as-a-Judge, and Human
- 28 - Current Challenges: Hallucinations, Cost, and Debugging
- 29 - Real-world Incidents and the AI Incident Database
- 30 - Career Impact: Which Jobs are Most at Risk?
- 31 - Software 3.0: The Evolution of Development Paradigms
- 32 - Weathering the Storm: Strategies for the Future
- 33 - Beyond LLMs: World Models and the Future of AMI
- 34 - Recommended Resources and Closing Thoughts
