// TAG: AI AGENTS

Kickstart your Kanbots project by setting up the foundational Tauri v2 desktop framework with a Svelte 5 frontend, creating a robust environment for …

Dive into Trigger.dev v4-beta, understanding its core concepts, setting up your first project, and building robust, scalable workflows for AI …

Discover AIPack, an open-source agentic runtime for building, running, and sharing AI agents. Learn installation, core concepts, and create your first …

Explore the fundamental need for memory in AI agents, understanding how it overcomes LLM limitations and enables more intelligent, stateful, and …

A structured overview of the most important and trending AI engineering topics in 2026, covering agent systems, context engineering, infrastructure, …

Explore the paradigm shift in AI engineering, moving from individual models to complex, orchestrated multi-agent systems. Understand AI workflow …

Explore the dynamic and critical field of AI security, understanding unique challenges, key threats like prompt injection and data poisoning, and the …

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 powerful paradigm of CLI-first AI systems. Learn how AI agents integrate into your terminal for command automation, scripting, and …

Discover the Model Context Protocol (MCP), an open standard for AI agent-tool integration. Learn its purpose, core concepts, and why it's crucial for …

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

Learn how to set up and run a basic, stateless AI agent using Google's Agent Development Kit (ADK) as the foundation for long-running systems.

Learn how to set up your Trigger.dev v4-beta environment, initialize a new project, and create your very first durable background job with …

Prepare your system for AI agent development with AIPack. This guide covers Python, AIPack CLI, Ollama for local models, and VS Code setup for an …

Explore the foundational components of modern AI agents: Large Language Models (LLMs) as the brain, Tools as their external capabilities, and Memory …

Dive into Model Context Protocol (MCP) tool schemas, learning how to define AI agent capabilities, integrate UI resources, and structure your tools …

Explore the fundamental building blocks of AI agents: perception, memory, planning, tool use, and communication. Understand how these core components …

Get started with AI-powered coding! This chapter guides you through setting up Cursor 2.6 and GitHub Copilot, explaining prerequisites, installation, …

Explore the fundamental memory concepts for AI agents: Working, Short-term, and Long-term Memory. Understand their distinct roles, how they overcome …

Learn how to interact with Large Language Models using AI APIs in Python, setting the foundation for building intelligent applications.

Learn how to leverage Git worktrees within Kanbots to provide isolated development environments for AI agents, enabling parallel and conflict-free …

Dive into AIPack by building your very first AI agent. Learn about the structure and purpose of .aip files, define multi-stage markdown agents, and …

Explore synchronous and asynchronous communication patterns in distributed systems, understanding their tradeoffs for scalability, resilience, and …

Explore the foundational concepts of Long-Term Memory for AI agents, focusing on Episodic and Semantic memory types. Learn how agents store and …

Dive into the core patterns for building multi-step AI agent workflows. Explore sequential, parallel, and graph-based orchestration, and understand …

Uncover the critical threat of Prompt Injection, the #1 vulnerability in LLM applications. Learn about direct and indirect attacks and initial defense …

Prepare your development environment for the Model Context Protocol (MCP) by setting up Node.js, TypeScript, and the MCP TypeScript SDK v2, with …

Learn to configure, deploy, and interact with your first Kiro agent for enhanced development workflow.

Learn the principles and practices of crafting effective prompts to guide Large Language Models (LLMs) for specific tasks.

Learn how to design and implement multi-stage markdown agents using AIPack, leveraging Lua for dynamic control flow and building robust AI workflows.

Explore the fundamentals of Retrieval-Augmented Generation (RAG) architectures, understand why they are crucial for production-ready LLM applications, …

Explore Agent Operating Systems (Agent OS), the foundational layer for building and managing intelligent AI agents, covering core components, …

Move beyond conversational AI to automate complex terminal tasks with AI agents. Learn about command generation, shell tool integration, and …

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

Learn how AI agents discover and register tools using the Model Context Protocol (MCP), focusing on tool manifests, discovery mechanisms, and …

Explore vector memory and embeddings, understanding how AI agents leverage numerical representations for efficient similarity-based information …

Learn how to extend LLM capabilities with tools and function calling for real-world applications.

Learn how to integrate an AI agent with A2UI to generate static user interfaces using the Python ADK.

Learn to orchestrate multi-agent AI workflows in Kanbots, assigning distinct personas for tasks like code generation and review using Git worktrees.

Learn how AI agents interact with external tools using the Model Context Protocol (MCP) and LangChain.js, focusing on tool invocation and schema …

Explore AI Orchestration Engines, their role in coordinating multi-agent systems, key components, and practical patterns for building complex, …

Dive into AutoGen, Microsoft's framework for building multi-agent systems that collaborate through conversational AI. Learn to define agent roles, …

Discover how AI agents can seamlessly integrate with your existing shell tools, leveraging pipes, redirects, and subprocess execution for powerful …

Explore how AI agents store their memories, from simple file systems to advanced vector databases, understanding the trade-offs and practical …

Learn how to integrate UI-driven tool calling into your React and React Native applications to empower AI agents.

Equip Kanbots with real-time AI agent progress, logging, and user controls like pause, resume, and cancel, enhancing user interaction with multi-agent …

Learn how to connect your AIPack agents to various AI models, including local setups with Ollama and popular cloud provider APIs, for powerful agentic …

Unlock enhanced productivity by integrating AI agents into your development and debugging workflows. Learn to automate commands, create dynamic …

Learn how to design and implement robust orchestration for complex AI workflows and multi-agent systems, enhancing scalability and reliability.

Explore how AI agents retrieve information from various memory types, focusing on strategies like keyword matching, vector similarity search, and …

Explore AI Tool Marketplaces, how they empower AI agents with external capabilities, and their role in modern AI orchestration and development.

Explore how Model Context Protocol (MCP) handles tool execution and request routing, enabling AI agents to interact with external tools efficiently …

Discover how to build, deploy, and manage intelligent AI agents and automated workflows using Trigger.dev v4-beta, integrating tools and …

Optimize your AIPack development using VS Code's agent customizations and the Multi-Agent Communication Protocol (MCP) for enhanced debugging and …

Explore Event-Driven Architectures to build reactive and scalable systems, understand core concepts like events, brokers, and consumers, and apply …

Dive into AI agents and automations, understanding how tools like Cursor 2.6 and GitHub Copilot leverage autonomous AI to streamline development …

Explore multi-agent workflows and AI-discoverable skills in CLI-first AI systems. Learn how to coordinate AI agents for complex tasks and empower them …

Learn to build a simple Retrieval Augmented Generation (RAG) agent that leverages vector memory and conversational history to provide informed and …

Dive deep into securing your AI agent integrations with Model Context Protocol (MCP). Learn about robust permissions, authorization flows, and …

Explore Semantic Kernel's architecture, including Skills and Planners, for building robust enterprise AI applications with Python.

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

Implement robust logging for AI agent activities within Kanbots and understand the crucial steps for packaging and deploying your cross-platform …

Learn how to integrate human decisions and real-time feedback into robust, durable Trigger.dev workflows for AI agents and collaborative systems.

Explore how to build complex AI agents by composing smaller, specialized agents and creating reusable skills with AIPack's modular architecture.

Explore advanced concepts and best practices for designing and implementing robust, scalable, and secure memory systems for AI agents in production …

Dive deep into creating robust tools for AI agents, integrating external APIs, handling complex inputs/outputs, error management, and asynchronous …

Explore advanced Model Context Protocol (MCP) concepts, including UI resources, asynchronous tool patterns, and secure deployment strategies for …

Explore best practices for designing and deploying CLI-first AI agents, understand critical security considerations, and envision the future trends …

Learn how to effectively test AWS Kiro agents for correctness, consistency, and reliability.

Unlock the full power of Trigger.dev by learning about the Managed Connector Platform (MCP) and how to build custom connectors for proprietary systems …

Explore how AI agent frameworks manage short-term and long-term memory, and track workflow state to build intelligent, conversational, and persistent …

Learn Runtime Protection for AI Agents: Live Defenses, covering active defenses like input/output moderation, tool access control, and behavioral …

Master debugging, optimizing, and preparing your AIPack AI agents for reliable, cost-effective production deployment. Learn about MCP server insights, …

Explore advanced architectural patterns and design principles for building robust, scalable, and intelligent AI agent systems, including Agent …

Get hands-on building your first autonomous AI agent using Python and LangChain. Learn to integrate LLMs, tools, and memory to create a smart research …

Master debugging, testing, and monitoring strategies for AI agent systems built with LangGraph, AutoGen, CrewAI, and Semantic Kernel to ensure …

Explore multi-agent AI workflows and automate pull requests with tools like Cursor 2.6 and GitHub Copilot, enhancing developer productivity for …

Learn how to integrate AWS Kiro into your CI/CD pipelines for automated code reviews and more.

Learn how to evaluate, observe, and debug AI agents for better performance and reliability.

Explore the critical aspects of testing, evaluating, and observing AI agents and multi-agent systems to ensure reliability, manage emergent behaviors, …

Compare leading AI agent frameworks like LangGraph, AutoGen, CrewAI, and Semantic Kernel. Understand their core architectures, strengths, and …

Learn how to debug and troubleshoot Kiro agents, including understanding logs, MCP insights, and AWS CloudWatch.

Learn to build an AI-powered customer support agent using Trigger.dev, integrating AI, human escalation, and durable workflows for robust, real-world …

Master best practices for designing, developing, debugging, and sharing robust, production-ready AI Packs using AIPack, focusing on modularity, …

Explore the evolution of AI architectures, focusing on Large Language Models (LLMs), Generative AI, and AI Agents. Learn patterns like RAG, …

Build an automated financial analysis assistant using CrewAI. Learn to define agents, integrate tools, manage tasks, and orchestrate a multi-step …

Build a practical, secure interaction layer for Large Language Models (LLMs) to protect against common vulnerabilities like prompt injection and …

Explore the cutting-edge future trends in AI engineering, including hyper-specialized agents, self-improving systems, and decentralized AI, alongside …

Explore the strategic implications of integrating sophisticated AI agents into enterprise operations, moving beyond automation to augmentation and …

Learn best practices for developing agent-driven UIs with A2UI, focusing on declarative generation and alignment.

Learn how to build an agent-driven UI workflow for task automation in React or React Native.

Learn how to optimize AWS Kiro for better performance, cost-effectiveness, and smarter AI solutions.

Build Kanbots, a desktop Kanban app orchestrating AI agents on cards using Git worktrees for isolated tasks. Learn multi-persona development workflows …

Master Kanbots: integrate AI agents like Claude/Codex, leverage git worktrees for isolated runs, and orchestrate multi-agent dev workflows with …

Master building production-ready long-running AI agents using ADK. Learn architectural design, implementation phases, and robust strategies for state …

Learn to build robust, long-running AI agents using Google ADK, capable of persisting state and context, allowing for pause, resume, and recovery.

Embark on a comprehensive journey to master Trigger.dev v4-beta, learning to build, deploy, and manage robust AI agents and automated workflows for …

Master AIPack from installation to production. Learn architecture, multi-stage agents, Lua logic, local/cloud models, VSCode workflows, and real-world …

Learn to build, run, and share robust AI agents for production workflows using AIPack, covering architecture, multi-stage agents, Lua logic, and VS …

Explore 3 production-style project ideas for on-device AI agents and tiny LLMs, leveraging modern edge AI tooling and frameworks as of 2026 for …

Explore the principles and practical applications of Agentic AI Systems, covering autonomous agents, planning, reasoning, tool usage, memory, and …

Learn to design and build sophisticated AI applications using modern agent frameworks like LangGraph, AutoGen, CrewAI, and Semantic Kernel, focusing …

Explore AI agent memory systems: vector, semantic, episodic, and long-term. Understand storage, retrieval, and memory-context trade-offs in agent …

Explore CLI-first AI systems, learning how AI agents integrate with terminal environments for automation, scripting, and enhanced developer workflows. …

Learn to integrate AI agents directly into your terminal workflows, automating command-line tasks, enhancing developer processes, and orchestrating …

Explore the next generation of AI engineering, covering AI workflow languages, agent operating systems, orchestration engines, and AI-native …

Explore leading AI agent frameworks like LangGraph, AutoGen, CrewAI, and Semantic Kernel. Master multi-step workflows, memory, and tool orchestration …

Learn to integrate AI agents with external tools using the Model Context Protocol (MCP). This guide covers tool schemas, registration, agent …

Explore the Model Context Protocol (MCP) for AI tool integration. Learn how AI agents define, register, and use tools, covering schemas, execution, …

Explore the essential role of memory in AI agents, covering different memory types, storage, retrieval, and how agents use them to learn and maintain …

Learn how to build intelligent AI agents using Java and Spring Boot with practical examples.

Learn to build intelligent AI agents using Java and Spring Boot with practical examples.