In this blog, I aim to provide a comprehensive list of valuable resources for learning Agentic AI, which refers to developing intelligent systems capable of perception, autonomous decision-making, reasoning, and interaction in dynamic environments. These resources include tutorials, research papers, online courses, and practical tools to help you deepen your understanding of this emerging field.
This blog will continue to be updated with relevant and popular papers periodically, based on emerging trends and the significance of newly published works in the field. Additionally, feel free to suggest any papers that you would like to see included in this list.
This curated list highlights some of the most impactful and insightful resources currently available. These resources have been chosen for their relevance, depth, and practical value in understanding and developing Agentic AI systems, making them invaluable for both beginners and seasoned professionals in the field.
Recommended Resources
- Arxiv Papers
- The Rise and Potential of Large Language Models-Based Agents: A Survey
Read the paper - Exploring Large Language Model-Based Intelligent Agents: Definitions, Methods, and Prospects
Read the paper - The Landscape of Emerging AI Agents Architectures for Reasoning, Planning, and Tool Calling: A Survey
Read the paper - AI Agents That Matter
Read the paper - Agent AI: Surveying the horizons of multimodal interaction
Read the paper
- The Rise and Potential of Large Language Models-Based Agents: A Survey
- Frameworks
- Agentarium: Agentarium is a user-friendly Python framework that makes it easy to create, manage, and coordinate AI agents. It helps you effortlessly handle multiple agents and their interactions in different settings.
- LangGraph: LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows.
- MOOC Courses (Free)
- Large language models agents: The course discusses some of the following topics:
- Fundamental concepts related to LLM agents, including the foundation of LLMs
- Reasoning & planning
- Retrieval augmented generation (RAG)
- Essential LLM abilities required for task automation
- Infrastructures for AI agent development
- Single-agent, multi-agent, human agent integrations
- Example agent applications include code generation, robotics, web automation, medical applications, scientific discovery, etc.
- Privacy, safety and ethics
- Evaluation & benchmarking
- Large language models agents: The course discusses some of the following topics:
The Role of LLMs in AI Agents
At the core of modern AI agents lies the power of large language models (LLMs). The resources listed above delve into various aspects of LLMs, offering insights into their architecture, capabilities, and practical applications in building sophisticated AI agents. These LLMs provide the foundational capabilities required for understanding, reasoning, and generating human-like responses. By leveraging vast datasets and advanced architectures, LLMs enable AI agents to perform complex tasks, interact intelligently with humans, and adapt to diverse applications. Their ability to integrate with tools and frameworks further enhances their utility, making them an indispensable component in the evolution of intelligent systems.
Explore these resources to kickstart or enhance your journey into the world of Agentic AI, and don’t hesitate to contribute by sharing your recommendations for additional papers or tools!
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I found it very helpful. However the differences are not too understandable for me