What you will learn?
Module 1: Python Foundations for Agentic AI
Module 2: AI & LLM Fundamentals
Module 3: Foundations of Agentic AI
Module 4: Tools & Function Calling
Module 5: Memory & RAG Systems
Module 6: Multi-Agent Systems
Module 7: Agent Frameworks
Module 8: Capstone Project
Module 9: Deployment,Monetization & Soft Skills
About this course
AgentForge AI™ – Agentic AI with Python
AgentForge AI™ is a structured 60-hour professional program designed to equip participants with practical expertise in designing and deploying Agentic AI systems using Python. The course provides a comprehensive foundation in modern AI development, with a strong focus on Large Language Models (LLMs), intelligent agent architecture, tool integration, memory systems, and multi-agent collaboration.
Unlike traditional AI courses that focus solely on theory, this program emphasizes applied learning through guided implementation, real-world case studies, and capstone project development. Participants will develop the capability to build AI agents that can reason, plan, utilize external tools, and execute tasks autonomously in business environments.
By the conclusion of the program, learners will be prepared to architect, develop, and deploy production-ready Agentic AI solutions aligned with current industry standards and emerging enterprise use cases.
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1. Python installation
2. Variables & data types
3. Loops & conditionals
4. Functions
1. Classes & objects
2. Working with files
3. JSON handling
4. Exception handling
1. How APIs work
2. Using requests library
3. API authentication
4. Intro to async
1. Connecting to LLM API
2. Sending prompts
3. Handling responses
4. Improving output formatting
1. AI vs ML vs DL
2. How LLMs work
3. Tokens & temperature
4. Context window
1. Zero-shot & few-shot
2. Chain of thought
3. System prompts
4. Output control techniques
1. Structured prompting
2. Creating use-case assistant
3. Testing & refining prompts
1. Agent vs chatbot
2. Agent components
3. ReAct framework
4. Thought-Action loop
1. Planner
2. Executor
3. Critic
4. Reflection loop
1. Implement reasoning loop
2. Task-based agent
3. Debugging agent behavior
1. What are tools?
2. Function calling schema
3. Tool selection logic
1. Calculator tool
2. File reader tool
3. Weather tool simulation
1. Automatic tool selection
2. Tool execution loop
3. Error handling
1. Build smart assistant
2. Test multiple tasks
3. Improve decision flow
1. Short-term memory
2. Long-term memory
3. Conversation tracking
1. What are embeddings?
2. Similarity search
3. Intro to RAG
1. Upload document
2. Retrieve context
3. Generate smart answers
1. Role-based agents
2. Planner agent
3. Executor agent
4. Critic agent
1. Task delegation
2. Message passing
3. Conflict resolution
1. Build research team agent
2. Generate structured report
1. Chains
2. Agents
3. Memory
1. Tool integration
2. Memory
3. Structured outputs
1. Creating roles
2. Task execution
3. Collaboration model
1. Conversational agents
2. Tool-enabled communication
3. Comparison of frameworks
1. Project selection
2. Architecture design
3. Workflow diagram
1. Core agent logic
2. Tools integration
3. Core agent logic
4. Tools integration
1. UI integration
2. Performance improvement
3. Presentation prep
1. Streamlit deployment
2. FastAPI backend
3. Hosting basics
1. SaaS pricing
2. Selling AI solutions
3. Freelancing with AI
4. Building AI startup roadmap
By the end of this session, students will.
1. Explain AI solutions clearly to non-technical people
2. Present AI projects confidently
3. Work effectively in teams
4. Handle clients professionally
5. Position themselves as AI professionals
