Agentic Handbook

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Contents

Chapter 1–Introduction to Agentic AI

1. 1.1 Evolution of AI to Agentic Systems
2. 1.2 Why Agents?
3. Agent Operations - Five Fundamental steps
4. 1.3 Chatbots vs True AI Agents
5. 1.4 Key Features of Agentic AI
6. 1.5 Differences with Traditional AI
7. Example Summarize a PDF Read all PDFs, create knowledge base, generate Q&A system

Chapter 2 - Single-agent vs. multi-agent architectures

Chapter 3 - RAG architecture

1. Agentic RAG
2. Agentic flow
3. Multi-agent architectures
4. Human-in-the-loop
5. Memory transformation

Chapter 4 - RAG systems

1. How RAG Works (Step-by-Step)
2. RAG Architecture
3. Practical Implementation of RAG using Lang chain

Chapter 5- My First Agent–Practical Implementation

1. pip install google-generativeai requests
2. Web-based weather chatbot (Streamlit)
3. Simple Agentic Code (Python + Gemini)
4. Web-based Agentic (Streamlit)
5. Langchain Agent
6. Implementing Agentic using Lang chain

Chapter 6 - Popular AI Agent Frameworks

1. LangChain :
2. AutoGen (Microsoft)

Chapter 7 - Types of AI Agents

1. Memory-Enhanced: The Personalized Powerhouses Long-term memory, personalization, adaptive learning Project management AI, personalized assistants Individualized experiences, long-term interactions
2. Fixed Automation–The Digital Assembly Line
3. Examples RPA for invoice processing, email autoresponders, basic scripting tools (Bash, PowerShell).
4. LLM-Enhanced Agents–Smarter, but Not Exactly Einstein
5. Examples Email filtering, AI-assisted content moderation, customer support request classification.
6. ReAct Agents–Reasoning Meets Action
7. Examples Language agents handling multi-step queries, AI game masters, project planning tools.
8. ReAct + RAG Agents–Grounded Intelligence
9. Examples Legal research systems, clinical decision support tools, technical troubleshooting assistants.
10. Tool-Enhanced Agents–The Multi-Taskers
11. Examples Code generation assistants (e.g., GitHub Copilot, Cody), developer terminals, data analysis bots combining multiple APIs.
12. Self-Reflecting Agents–The Philosophers
13. Examples AI systems that justify their outputs, self-evaluating learning agents, quality assurance (QA) bots.
14. Memory-Enhanced Agents–The Personalized Powerhouses
15. Examples Project management assistants with task history, customer support agents tracking interactions, personalized recommendation systems.

Chapter 8 - Why Most AI Agents Fail & How to fix them

1. 1. Development-Related Challenges
2. 2. LLM-Centric Challenges
3. 3. Production and Operational Challenges
4. Development Issues in AI Agents
5. Missing Feedback Loops Without feedback mechanisms, agents cannot learn from mistakes or improve over time. • Incorporate feedback loops to enable continuous refinement
6. LLM-Related Challenges
7. High Cost of Running LLMs
8. Planning Challenges in AI Agents
9. Reasoning Limitations in AI Agents
10. Tool Invocation Challenges
11. Scaling AI Agents

Chapter 9 - Governance considerations

1. Continuity and Resilience Define fallback and manual intervention procedures to sustain critical operations. Preserve business continuity, protect data integrity, and enable human takeover when required.
2. Foundations for AI agent evaluation and governance: progressive governance practices
3. Pre-Deployment Validation and Governance of AI Agents

Conclusion

About the Author

Contents

Chapter 1–Introduction to Agentic AI

1. 1.1 Evolution of AI to Agentic Systems
2. 1.2 Why Agents?
3. Agent Operations - Five Fundamental steps
4. 1.3 Chatbots vs True AI Agents
5. 1.4 Key Features of Agentic AI
6. 1.5 Differences with Traditional AI
7. Example Summarize a PDF Read all PDFs, create knowledge base, generate Q&A system

Chapter 2 - Single-agent vs. multi-agent architectures

Chapter 3 - RAG architecture

1. Agentic RAG
2. Agentic flow
3. Multi-agent architectures
4. Human-in-the-loop
5. Memory transformation

Chapter 4 - RAG systems

1. How RAG Works (Step-by-Step)
2. RAG Architecture
3. Practical Implementation of RAG using Lang chain

Chapter 5- My First Agent–Practical Implementation

1. pip install google-generativeai requests
2. Web-based weather chatbot (Streamlit)
3. Simple Agentic Code (Python + Gemini)
4. Web-based Agentic (Streamlit)
5. Langchain Agent
6. Implementing Agentic using Lang chain

Chapter 6 - Popular AI Agent Frameworks

1. LangChain :
2. AutoGen (Microsoft)

Chapter 7 - Types of AI Agents

1. Memory-Enhanced: The Personalized Powerhouses Long-term memory, personalization, adaptive learning Project management AI, personalized assistants Individualized experiences, long-term interactions
2. Fixed Automation–The Digital Assembly Line
3. Examples RPA for invoice processing, email autoresponders, basic scripting tools (Bash, PowerShell).
4. LLM-Enhanced Agents–Smarter, but Not Exactly Einstein
5. Examples Email filtering, AI-assisted content moderation, customer support request classification.
6. ReAct Agents–Reasoning Meets Action
7. Examples Language agents handling multi-step queries, AI game masters, project planning tools.
8. ReAct + RAG Agents–Grounded Intelligence
9. Examples Legal research systems, clinical decision support tools, technical troubleshooting assistants.
10. Tool-Enhanced Agents–The Multi-Taskers
11. Examples Code generation assistants (e.g., GitHub Copilot, Cody), developer terminals, data analysis bots combining multiple APIs.
12. Self-Reflecting Agents–The Philosophers
13. Examples AI systems that justify their outputs, self-evaluating learning agents, quality assurance (QA) bots.
14. Memory-Enhanced Agents–The Personalized Powerhouses
15. Examples Project management assistants with task history, customer support agents tracking interactions, personalized recommendation systems.

Chapter 8 - Why Most AI Agents Fail & How to fix them

1. 1. Development-Related Challenges
2. 2. LLM-Centric Challenges
3. 3. Production and Operational Challenges
4. Development Issues in AI Agents
5. Missing Feedback Loops Without feedback mechanisms, agents cannot learn from mistakes or improve over time. • Incorporate feedback loops to enable continuous refinement
6. LLM-Related Challenges
7. High Cost of Running LLMs
8. Planning Challenges in AI Agents
9. Reasoning Limitations in AI Agents
10. Tool Invocation Challenges
11. Scaling AI Agents

Chapter 9 - Governance considerations

1. Continuity and Resilience Define fallback and manual intervention procedures to sustain critical operations. Preserve business continuity, protect data integrity, and enable human takeover when required.
2. Foundations for AI agent evaluation and governance: progressive governance practices
3. Pre-Deployment Validation and Governance of AI Agents

Conclusion

About the Author