WEEK 1: Foundations of AI Agents

Understand how agents work

1. What are AI Agents (vs ChatGPT)
2. Agent lifecycle: Observe → Think → Act
3. Components:

• LLM (brain)
• Memory
• Tools
• Orchestrator

1. Why agents are different from normal AI
2. Real-world use cases

WEEK 2: LLMs + Prompt Engineering (Core Skill)

Goal: Control AI behavior

1. Prompt engineering (advanced)
2. System prompts vs user prompts
3. Handling hallucinations
4. Token limits & context windows
5. Build smart prompts for:

• Chatbots
• Automation

WEEK 3: AI Agent Architecture (Core Engineering)

 Goal: Design agents

1. Agent architecture (ReAct, Plan-Execute)
2. Task planning & reasoning
3. Tool usage (APIs, search, calculators)
4. Workflow design

 WEEK 4: Tools & Frameworks ( MOST IMPORTANT)

Build real agents

1. LangChain
2. CrewAI
3. AutoGen
4. OpenAI API
5. Connect:

• AI + Google search
• AI + APIs
• AI + external tools

 WEEK 5: Memory + RAG (Advanced Agents)

Make agents “smart”

1. Memory systems:

• Short-term
• Long-term

1. Retrieval-Augmented Generation (RAG)
2. Vector databases

Build:
 AI agent that remembers conversations

WEEK 6: Multi-Agent Systems (NEXT-LEVEL)

Build teams of AI

• Multi-agent collaboration
• Role-based agents
• Task delegation
• Agent communication

 Build:
Team of agents (researcher + writer + reviewer)

 WEEK 7: Deployment + Automation

Make agents useful

1. Deploy agents (web apps / APIs)
2. Automation workflows
3. Scheduling tasks
4. Monitoring + debugging

Build:
 AI automation system (like business assistant)

WEEK 8: Final Project ( Portfolio)

Job-ready project

Choose ONE:

1. AI Research Agent
2. AI Business Automation Agent
3. AI Content Creation System
4. Personal AI Assistant