From the moment I discovered what AI can do, I was hooked. With over 20+ years of experience in IT, I’ve always been fascinated by how technology can drive change.

As the Director of Azure Platform  at Estée Lauder Companies, I am focused on designing and building scalable intelligent systems that fuel insights, complement decision-making and drive growth through AI and Data.

Every day is an opportunity to leverage cutting-edge technologies to help businesses navigate the ever-changing digital landscape.

If you're passionate about AI, Cloud and Data  feel free to reach out. 

Let's explore how we can collaborate.

𝟏𝟐 𝐒𝐭𝐞𝐩𝐬 𝐭𝐨 𝐁𝐮𝐢𝐥𝐝 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐓𝐡𝐚𝐭 𝐀𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐓𝐡𝐢𝐧𝐤, 𝐏𝐥𝐚𝐧, 𝐚𝐧𝐝 𝐄𝐱𝐞𝐜𝐮𝐭𝐞

Here is a step-by-step Agentic AI Learning Roadmap designed to take you from zero to advanced AI agent development:

𝟏. 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈
Start with the fundamentals. Understand what powers models like GPT, Claude, and Gemini.
Learn about diffusion, embeddings, fine-tuning, and limitations of LLMs.

𝟐. 𝐁𝐚𝐬𝐢𝐜 𝐂𝐨𝐝𝐢𝐧𝐠 𝐟𝐨𝐫 𝐀𝐈
You do not need to be a hardcore coder, but knowing the basics helps.
Learn Python syntax, APIs (OpenAI, Anthropic, Groq), JSON handling, debugging, and scripting using Jupyter notebooks.

𝟑. 𝐋𝐋𝐌 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥𝐬
Understand how large language models actually *work*.
Learn about temperature, sampling, embeddings, reasoning, and safety constraints (guardrails).

𝟒. 𝐑𝐀𝐆 𝐒𝐲𝐬𝐭𝐞𝐦 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥𝐬
Learn how to give AI context and memory.
Understand embeddings, vector databases, indexes, and query retrieval.
Start with small RAG projects like “Chat with PDFs.”

𝟓. 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐋𝐚𝐧𝐠𝐂𝐡𝐚𝐢𝐧
Explore the most widely adopted agentic framework.
Learn about memory, tools, agents, chains, and patterns like ReAct and MRKL.

𝟔. 𝐏𝐫𝐨𝐦𝐩𝐭 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥𝐬
Master structured prompting.
Learn role prompting, guardrails prompting, chain-of-thought prompting, and multi-agent coordination prompts.

𝟕. 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬
Move from “AI answers questions” to “AI completes tasks.”
Learn agent reasoning, task planning, self-healing loops, and real-world automation use cases.

𝟖. 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐃𝐞𝐬𝐢𝐠𝐧 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬
Learn architecture not just code.
Understand task decomposition, retrieval-augmented agents, orchestration layers, and stage management using LangGraph.

𝟗. 𝐁𝐮𝐢𝐥𝐝 𝐘𝐨𝐮𝐫 𝐅𝐢𝐫𝐬𝐭 𝐍𝐨-𝐂𝐨𝐝𝐞 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭
Use OpenAI Assistants, Flowise, or Zapier AI Actions to build your first working agent like a meeting summarizer or support bot.

𝟏𝟎. 𝐁𝐮𝐢𝐥𝐝 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐑𝐀𝐆 𝐒𝐲𝐬𝐭𝐞𝐦𝐬
Combine RAG + Agentic reasoning.
Experiment with multi-document retrieval, error-handling strategies, and memory optimization.

𝟏𝟏. 𝐋𝐚𝐧𝐠𝐂𝐡𝐚𝐢𝐧, 𝐂𝐫𝐞𝐰𝐀𝐈, 𝐋𝐚𝐧𝐠𝐆𝐫𝐚𝐩𝐡, 𝐀𝐮𝐭𝐨𝐆𝐞𝐧
Learn production-level agent orchestration.
Focus on multi-agent collaboration, team workflows, and error recovery loops.

𝟏𝟐. 𝐁𝐮𝐢𝐥𝐝 𝐚𝐧 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭 𝐟𝐫𝐨𝐦 𝐒𝐜𝐫𝐚𝐭𝐜𝐡 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧
Finally, go deep.
Build everything manually tool calling, reasoning, planning, logging, and observability with a simple state machine architecture.

This roadmap is not just about learning AI it is about building the *next generation* of intelligent systems.

𝐖𝐡𝐞𝐫𝐞 𝐚𝐫𝐞 𝐲𝐨𝐮 𝐜𝐮𝐫𝐫𝐞𝐧𝐭𝐥𝐲 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐣𝐨𝐮𝐫𝐧𝐞𝐲 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠, 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠, 𝐨𝐫 𝐒𝐜𝐚𝐥𝐢𝐧𝐠?

#AgenticAI #AIEngineering #GenAI

Sivasankar Natarajan

Technical Director | GenAI Practitioner | Azure Cloud Architect | Data & Analytics | Solutioning What’s Next

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𝐓𝐫𝐚𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐑𝐀𝐆 𝐯𝐬 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐯𝐬 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐑𝐀𝐆: 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐭𝐡𝐞 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞?

In the world of AI, three approaches stand out when it comes to task execution and automation: Traditional RAG, Agentic AI, and Agentic RAG. Understanding the differences is essential to building more adaptive and intelligent systems.

𝟏. 𝐓𝐫𝐚𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐑𝐀𝐆

Traditional RAG follows a linear pipeline, perfect for repetitive, predefined tasks.

* Key Features:

* Fixed task flow: The process is simple and follows a strict order.
* Predefined objectives: Tasks are based on known parameters with little to no change.
* Data collection and refinement: Data is gathered, refined, and then used to train the model.
* Outcome: Once the model is deployed, results are generated without self-adjustment.

Use Case: Best for static data and predictable tasks where accuracy is needed but no dynamic feedback is required.

---

𝟐. 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈

Agentic AI enables more dynamic decision-making through large language models and integrated APIs. It’s designed to perform in complex, multi-step workflows with a focus on continuous improvement.

* Key Features:

* Objective Setting: AI sets its own tasks and objectives.
* Self-Decisions: The AI can choose which tools or models to use based on real-time needs.
* Continuous Improvement: The AI learns and evolves as it works.

Use Case: Ideal for evolving tasks where the system must adapt, choose the best models, and adjust over time for optimized performance.

---

𝟑. 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐑𝐀𝐆

Agentic RAG combines retrieval-augmented generation with agent-led choices to create smarter, more adaptable workflows. It dynamically adapts based on the task at hand, ensuring real-time data fetching, multi-step logic, and self-improvement.

* Key Features:

* Fetching Useful Data: It uses API calls and vector search for the most relevant results.
* Multistep Logic: The process involves several steps, adjusting dynamically as it receives feedback.
* Self-Evolving: Continuously refreshes memory, adapting to new data and optimizing future tasks.
* Task Goal Setting: The system sets and revises its objectives to ensure the outcome is in line with what’s needed.

Use Case: Best for tasks that require real-time adaptation to changing inputs, offering the ability to evolve and fine-tune tasks as they progress.

---

𝐊𝐞𝐲 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞𝐬:

* Traditional RAG: Linear and static.
* Agentic AI: Dynamic with self-improvement.
* Agentic RAG: Combines both - adapts and improves based on evolving tasks and real-time inputs.

𝐖𝐡𝐢𝐜𝐡 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐝𝐨 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤 𝐡𝐨𝐥𝐝𝐬 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭 𝐩𝐫𝐨𝐦𝐢𝐬𝐞 𝐟𝐨𝐫 𝐧𝐞𝐱𝐭-𝐠𝐞𝐧 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬?

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