Agentic AI: Beginners Bootcamp
Curriculum
- 14 Sections
- 75 Lessons
- 10 Weeks
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- 1. Introduction to LLMs3
- 2. Prompt Engineering Essentials3
- 3. Python Essentials for Agent Workflows3
- 4. Function Calling & Tools11
- 4.1Defining a regular is-even function
- 4.2Calling is-even with LLM
- 4.3Using LLM function tools in prompts
- 4.4Enhancing function tools with power
- 4.5Defining multiple tools together
- 4.6Calling multiple tools in sequence
- 4.7Follow-up prompt generation using functions
- 4.8Validating functions
- 4.9Chat-driven function validation
- 4.10Advanced multi-function workflows
- 4.11Auto mode, any mode, fallback mode in function calling
- 5. LangChain Foundations6
- 6. LangGraph Fundamentals9
- 6.1Is-even check with LangGraph
- 6.2Two-node and three-node LangGraph workflows
- 6.3If-else branching logic in LangGraph
- 6.4Conditional branching with pass/fail
- 6.5Retry logic and looping patterns
- 6.6Intent-based chatbot using LangGraph
- 6.7Selecting tools in workflows
- 6.8Streaming response agent logic
- 6.9Integrating LLMs with LangGraph
- 7. Agentic AI Design Patterns9
- 8. Multi-Agent Orchestration8
- 8.1Central orchestrator design
- 8.2Orchestrator vs. Task agents
- 8.3Managing agent states
- 8.4Communication via shared memory/state
- 8.5Role-based agents: Searcher, Synthesizer, Executor
- 8.6Multi-agent collaboration (Amazon, Flipkart, Sapna examples)
- 8.7Message state and orchestration control
- 8.8Vendor APIs integration logic
- 9. Use Case: Ordering Agents5
- 10. Use Case: Delivery Agents3
- 11. Capstone Project – Build an End-to-End Agentic System7
- 12. User Interfaces for Agents5
- 13. Prompt Tuning Parameters3
- 14. Capstone Project: Zerodha MCP server0
Balancing creativity and accuracy
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