// TAG: AGENTIC AI

Understand the landscape of on-device AI agents and tiny LLM systems, set up your development environment, and explore core tooling for edge AI.

Dive into the exciting world of Agentic AI. Understand what autonomous AI agents are, why they matter, and their core components like planning, …

Discover the foundational concepts of AI agents, their architecture, and why they represent a paradigm shift in building intelligent applications …

Explore the transformative world of AI-augmented development, differentiating between interactive AI copilots and autonomous AI agent systems, and …

Learn the fundamentals of Applied AI with Python, setting up your environment, and cultivating system thinking.

An introduction to Agentic Lightening, an open-source framework for optimizing AI agents with minimal code changes.

Master the art of crafting precise and secure prompts using system messages, effective delimiters, and structured output control for reliable LLM …

Dive into the OWASP Top 10 for LLM/Agentic applications (2025/2026), understanding critical vulnerabilities and strategies to build secure AI systems.

Discover how Large Language Models (LLMs) serve as the 'brain' for autonomous AI agents, enabling reasoning, planning, and decision-making through API …

Explains the core concepts of Agentic Lightening, including LitAgent, AgentLightningServer, Trainer, and LightningStore.

Unlock robust LLM reasoning with Chain-of-Thought and Self-Consistency. Learn to guide LLMs through complex problems, improving accuracy and …

Dive into AI Workflow Languages, understanding how they define, manage, and execute complex sequences of AI models, tools, and logic for robust …

Discover how to equip your autonomous AI agents with external tools and APIs, enabling them to interact with the real world, perform actions, and …

Connect on-device Speech-to-Text (STT) output to a local TinyLLM for intent recognition and entity extraction, forming the core of your AI agent.

Dive deep into the planning and task decomposition mechanisms that enable autonomous AI agents to break down complex goals into manageable steps. …

Dive into LangGraph to build dynamic, stateful AI agent workflows. Learn about state machines, graph nodes, and edges for complex agent orchestration …

Learn how to implement real-time UI updates with streaming intelligence in your frontend applications.

Dive deep into the reasoning core of autonomous AI agents. Learn how agents plan, solve problems, make decisions, and leverage advanced architectures …

Learn how to implement a Retrieval-Augmented Generation (RAG) system to enhance LLMs with external knowledge.

Learn how to add interactivity and manage state in A2UI-driven user interfaces.

Unpack the core components of an Agentic AI system: the LLM brain, crucial memory, external tools, and intelligent planning mechanisms. Learn how …

Explore agentic AI security, focusing on tool misuse and insecure output handling. Learn to protect AI systems and design safe, production-ready AI …

Explore short-term memory in Agentic AI systems, focusing on LLM context windows, conversation history management, and practical Python …

Learn how to build UIs that collaborate with AI agents, managing their state and interactions.

Learn how to orchestrate complex AI agents using popular frameworks like LangChain and LlamaIndex, integrating LLMs, tools, and memory for …

Explore how autonomous AI agents gain long-term knowledge using Retrieval-Augmented Generation (RAG) and vector databases. Learn about embeddings, …

Explore agentic retrieval, a paradigm where LLMs act as intelligent agents to plan and execute complex information retrieval tasks, going beyond …

Learn about the Model Context Protocol (MCP) in AWS Kiro, its role in facilitating communication and context sharing among AI agents.

Learn to build and understand AI agents that perceive, reason, and act autonomously using Python and LLMs.

Learn how to optimize a basic QA agent using Agentic Lightening's Automatic Prompt Optimization (APO) technique.

Extend your AI agents' capabilities by integrating custom tools and external APIs to access real-time data and perform actions beyond their core LLM …

Dive deep into advanced agent architectures like ReAct, Reflection, and iterative planning-execution loops to build more robust and autonomous AI …

Learn how to proactively identify, analyze, and mitigate security threats in AI systems, especially Large Language Models and agentic applications, …

Learn how to enhance a LangChain agent with Reinforcement Learning using Agentic Lightening for better decision-making and tool usage in multi-step …

Explore persistent agent memory, distinguishing between short-term context and long-term knowledge bases for robust, production-ready AI agents. Learn …

Explore the world of multi-agent systems, learning how to design, coordinate, and orchestrate multiple autonomous AI agents to solve complex problems, …

Learn how to design and orchestrate complex AI-driven workflows using multi-agent systems.

Dive into advanced design patterns for building robust, scalable, and reliable AI agents ready for production environments.

Learn to rigorously evaluate and test your prompts and AI agents for accuracy, reliability, cost-efficiency, and safety in production environments.

Learn how to design, deploy, and manage production-ready autonomous AI agents, covering best practices for robustness, security, scalability, and …

Learn to optimize the cost and latency of your AI and agentic solutions, exploring techniques for token management, model selection, caching, and …

Take your AI agents from prototype to production. Learn critical strategies for scaling, optimizing costs, and ensuring ethical and responsible …

Explore the critical ethical considerations and robust control mechanisms essential for designing, deploying, and managing autonomous AI agents safely …

Learn about the unique security threats, privacy concerns, and ethical considerations in developing agentic AI systems using LLMs.

Learn how to use AWS Kiro to enhance a Python Flask web application with agent-driven development.

Learn to build a Smart Research Assistant Agent using agentic AI principles and tools.

Learn to build an autonomous workflow agent using Python and LLM APIs.

Comprehensive overview of agentic AI patterns and their role in building intelligent systems.

Agentic AI is rapidly moving beyond assistive tools to autonomously transform core software engineering functions, creating both significant …

Master prompt engineering & agentic AI for developers. This 2026 guide focuses on real-world production workflows, taking you from beginner to …

Learn to build and deploy advanced AI applications using prompt engineering and agentic AI workflows, focusing on practical, production-ready …

Explore Agentic AI Systems in 2026, covering autonomous agent architectures like ReAct, planning, memory, multi-agent coordination, and real-world …

Learn to secure AI systems, including Large Language Models (LLMs) and agentic applications, by understanding and mitigating prompt injection, data …

Explore modern Retrieval-Augmented Generation (RAG 2.0) systems, mastering hybrid search, GraphRAG, multi-hop retrieval, and agentic strategies to …

Comprehensive comparison of Akka Agentic AI and LangChain - features, performance, pros & cons, and when to use each for LLM orchestration and agentic …

Explore integrating on-device AI with Core ML, leveraging cloud-based AI APIs, and designing agentic features for intelligent user interactions in iOS …

Learn to integrate AI and agentic AI into React and React Native frontend applications for smarter, more engaging user experiences.

A comprehensive guide to mastering Applied and Agentic AI, from foundational programming to building intelligent systems.

Learn how to use Agentic Lightening, Microsoft's open-source framework for training and optimizing AI agents.

An advanced guide for developers to master the intricacies of building, deploying, and managing production-ready agentic AI systems.

A comprehensive guide for mastering advanced agentic AI systems in production, covering challenges and best practices.

A comprehensive guide to building intelligent AI agents using LangChain and LangGraph, covering foundational concepts, tool integration, memory …

A comprehensive, beginner-friendly guide to understanding, building, and applying agentic AI for UI and backend systems using popular libraries like …

A comprehensive, beginner-friendly guide to understanding, building, and applying agentic AI for UI and backend systems using popular libraries like …

A comprehensive and practical guide to MLOps and LLMOps principles and practices for managing the lifecycle of Large Language Models and Agentic AI …