As AI agents increasingly take center stage in modern applications—from intelligent assistants to autonomous decision-makers—the need for a structured learning path has never been more pressing. Aishwarya Srinivasan's AI Agents Learning Roadmap is a goldmine for developers, researchers, and enthusiasts eager to systematically master the skills needed to build and deploy intelligent agents.

📌 Don’t forget to save or bookmark this roadmap—it's a practical compass for your AI agent journey!

📸 Visual Reference: The Full Learning Roadmap

🧱 Level 1: Foundations of GenAI and Transformers

Start by understanding the fundamentals:

• What is Generative AI?
• Key concepts behind LLMs: attention, positional encoding, decoder stacks
• Pre-training vs. fine-tuning
• Common architectures: GPT, T5, BERT
• Tokenization (BPE, SentencePiece)

🧠 Goal: Build conceptual clarity on how language models work under the hood.

🧠 Level 2: Language Model Behavior and Prompting

Now that you know how LLMs are built, learn how to interact with them:

• Prompt engineering basics: zero-shot, few-shot, chain-of-thought
• Personalization and tool-use prompting
• Popular prompting methods (ReAct, Tree-of-Thought, WebGPT)
• Model evaluation: perplexity, top-k/p sampling

🧠 Goal: Become proficient in crafting prompts and understanding model behavior.

🔍 Level 3: Retrieval-Augmented Generation (RAG)

Unlock the power of external knowledge:

• What is RAG and why is it useful?
• Chunking strategies: semantic, fixed-size, recursive
• Embedding models: OpenAI, Cohere, BGE, Jina
• Vector stores: FAISS, Weaviate, LanceDB
• Evaluating RAG outputs

🧠 Goal: Master knowledge retrieval pipelines for more factual and grounded outputs.

🛠️ Level 4: LLMOps and Tool Integration

Bridge your models with real-world applications:

• LLMOps vs. MLOps
• Tool integration (LangChain, Dust, Marvin, CrewAI)
• Tool calling: JSON mode, function calling
• Auto tool selection and dynamic routing
• Synthetic data generation

🧠 Goal: Build flexible pipelines using tools and frameworks to manage LLM workflows.

🧠 Level 5: Agents and Agentic Frameworks

What are agents, and why do we need them?

• Simple and advanced agent construction
• Planning, tool-use, recursion
• Popular agent frameworks: LangGraph, CrewAI
• Action-observation loops
• Evaluation using LM-as-a-Judge

🧠 Goal: Understand and implement AI agents that can autonomously reason and act.

🗂️ Level 6: Agent Memory, State & Orchestration

Make agents more context-aware:

• Types of memory: buffer, summary, entity
• Episodic vs persistent memory
• Memory updates and orchestration in LangGraph
• Combining multiple memory types

🧠 Goal: Equip agents with memory and state-tracking for advanced multi-turn use cases.

🤝 Level 7: Multi-Agent Systems and Collaboration

Time to scale up:

• Multi-agent architectures: Hub-and-Spoke, Hierarchical
• Collaboration and communication protocols
• Conflict resolution between agents
• Use cases: dev teams, research assistants, finance bots

🧠 Goal: Design systems where multiple agents work together efficiently.

🔁 Level 8: Evaluation, Feedback Loops, and RL

Make agents smarter over time:

• LLM-as-a-Judge methods: LUNA-2, OpenAI evals
• RLHF, RLAIF, RLVF—when and how to apply
• Grading and fine-tuning agents with feedback

🧠 Goal: Learn how to evaluate and improve agents iteratively.

🛡️ Level 9: Protocols, Safety, and Alignment

Build safe and robust agents:

• Constitutional AI and red teaming
• Guardrails: GuardrailsAI, NeMo
• Traceability and logging
• Self-verifying agents

🧠 Goal: Ensure agents are safe, aligned, and trustworthy in complex environments.

🚀 Level 10: Build, Optimize & Deploy in Production

Take your solution to the real world:

• Deployment: Gradio, Streamlit, FastAPI
• Optimization: quantization, distillation
• Cost vs. performance tradeoffs (e.g., CPUs vs GPUs)
• Observability: LangSmith, Weights & Biases
• Prompt caching and vector cache optimization

🧠 Goal: Ship production-ready AI agents at scale.

🔚 Final Thoughts

This roadmap is more than just a list—it's a journey from understanding the core principles of language models to building fully autonomous, collaborative, and production-grade agents. Each level builds upon the previous, helping you become a holistic AI agent developer.

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