#ai-agents (231)
- Mastering Loop Engineering: Building Autonomous AI Agent Workflows
Explore loop engineering, the evolution from prompt engineering, to build production-grade autonomous AI agent workflows with goal-driven loops, tool use, and human oversight.
- Introduction to Loop Engineering: The Autonomous Agent Paradigm
Explore Loop Engineering as the evolution of prompt engineering, enabling AI agents to execute goal-driven workflows with tool access, feedback, and human checkpoints.
- Architecting Robust AI Agent Execution Loops
Learn to architect production-grade AI agent execution loops, integrating tools and human oversight for robust, goal-driven autonomous workflows.
- Loop Engineering: Integrating Tools & APIs for Autonomous Agents
Learn to design and build production-grade autonomous AI agents that use loop engineering to integrate tools and APIs for complex, real-world tasks.
- Designing Memory and State Management for Autonomous AI Agents
Learn to design and implement memory and state management systems that enable autonomous AI agents to achieve persistent understanding and robust, long-running workflows.
- Designing Autonomous AI Agent Workflows with Loop Engineering
Readers will learn to design robust, autonomous AI agent workflows using multi-agent systems and advanced loop engineering principles.
- Scaling, Resilience, and Cost Optimization for Production Agents
Explore scaling, resilience, and cost optimization for AI agents, transforming prompt engineering into robust, production-grade autonomous workflows with practical architectural insights.
- Cloud Infrastructure for Autonomous AI Agent Workflows
Learn to design and implement robust cloud infrastructure for deploying and managing autonomous AI agent workflows, covering scaling, observability, and human oversight.
- Operationalizing AI Agents: Observability, Security, Access
Learn to implement robust observability, security, and access control for autonomous AI agent workflows, ensuring safe and efficient production deployments.
- Engineering Autonomous AI Agents with Iterative Loops
Learn to architect and build production-grade autonomous AI agents by orchestrating continuous, goal-driven behaviors with iterative execution loops and human oversight.
- Loop Engineering: Autonomous AI Agent Workflows
Explore Loop Engineering, the next evolution after prompt engineering. AI agents build autonomous, production-grade coding workflows via goal-driven execution, tools, and human oversight.
- 8 Search APIs for Agentic AI RAG Systems: Complete Comparison 2026
Comprehensive comparison of top search APIs for agentic AI RAG systems in 2026 - features, performance, pros & cons, and when to use each for optimal agent performance.
- Harness Engineering for AI Coding Agents: A Practical Guide
Learn to build reliable, production-grade AI coding agents by mastering systematic environment design, state management, evaluation, and control systems.
- Harness Engineering for Production-Grade AI Agents
Learn to design, build, and maintain robust AI agent systems using Harness Engineering principles, ensuring reliability and production readiness.
- Systematic Environment Design for Reproducible Agents
Learn how to design systematic and reproducible environments for AI coding agents, ensuring consistent behavior and reliable performance in complex development workflows.
- Setting Up Your Agent Development Environment
Set up a robust development environment for AI coding agents, covering Python, virtual environments, version control, and API access for reproducible and reliable agent development.
- Build Control Systems to Guide AI Agent Actions and Tool Use
Implement effective control systems for AI agents to guide their actions, manage tool usage, and ensure reliable, predictable behavior.
- Agent State Management: Keeping Track of Context and Progress
Learn how to manage an AI agent's state, context, and progress systematically to ensure reliable and consistent behavior across interactions and complex tasks.
- Context Engineering: Optimizing Prompts and Tool Definitions
Master Context Engineering for AI coding agents: learn to optimize prompts and define tools effectively, ensuring your agents receive clear, actionable instructions and perform reliably.
- Implement Evals Frameworks for AI Agent Reliability
Design and implement robust evaluation frameworks to systematically measure and improve AI agent reliability, performance, and overall dependability.
- Observability for Agentic Systems: Seeing Inside the Black Box
Discover how to implement robust observability for AI coding agents, including structured logging, tracing, and metrics, to understand and debug complex agent behaviors.
- Testing AI Agents: Adapting Software Engineering Principles
Discover how to systematically test AI agents by adapting software engineering principles and building robust evaluation frameworks.
- Develop Long-Term Memory for AI Agents with Knowledge Retrieval
Learn to equip AI agents with long-term memory by leveraging external knowledge bases, vector databases, and Retrieval Augmented Generation for robust performance.
- Building a Production-Grade AI Coding Agent Harness (Project)
Build a complete, production-grade harness for an AI coding agent, integrating environment setup, state management, control loops, tools, evaluation, and observability.
- Harness Engineering for AI Agents
Master Harness Engineering for AI coding agents. Learn to design reliable agentic tools through systematic environments, state management, verification, and robust control systems.
- Architecting AI Agent Communication with the Agent Client Protocol
System designers will learn how the Agent Client Protocol standardizes AI agent-IDE communication for modular, interoperable development.
- Foundational Protocols for Agentic Developer AI Workflows
Understand how Agent Client Protocol and Model Context Protocol standardize AI agent integration, enabling advanced agentic developer workflows in IDEs.
- Understanding ACP and MCP in Agentic IDE Workflows
Learn to differentiate the Agent Client Protocol and Model Context Protocol, understanding their unique contributions to AI agent integration in IDEs.
- Standardizing IDE-Agent Communication with Agent Client Protocol
Understand how the Agent Client Protocol standardizes communication between IDEs and AI agents, fostering an open ecosystem for agentic developer workflows.
- Zed Editor's ACP: Deconstructing AI Agent Communication Flow
Understand Zed Editor's Agent Client Protocol implementation, its end-to-end request flow, and architectural implications for seamless AI agent integration.
- Architecting AI Agents for IDEs with the Agent Client Protocol
Learn how the Agent Client Protocol standardizes IDE-agent communication, enabling flexible agentic developer workflows and seamless AI integration.
- Scaling, Resilience, and Observability for AI Agents
Learn to architect scalable, resilient, and observable AI agent workflows, ensuring effective integration into production developer environments.
- ACP: Standardizing Secure AI Agent Integration in IDEs
Discover how the Agent Client Protocol standardizes AI agent integration in IDEs, covering its architecture, security, and critical design tradeoffs.
- How Zed's ACP Standardizes AI Agent Communication in IDEs
Zed Editor's Agent Client Protocol standardizes real-time communication between IDEs and AI coding agents, contrasting it with MCP for advanced workflows.
- Mastering Flue: Building Production-Ready AI Agents with TypeScript
Learn to build, deploy, and manage robust AI agents using the Flue Framework, focusing on its unique agent harness architecture, state management, and production deployment with TypeScript.
- Integrate AI into Your Terminal with omp.sh
Readers will learn how to integrate omp.sh, an AI coding agent, into their terminal workflow to generate, modify, and debug code efficiently.
- Welcome to Flue: The Agent Harness Architecture Explained
Explore Flue, the agent harness framework, understanding its architecture, how it differs from LLM SDKs, and build your first deployable AI agent in TypeScript.
- Deploying Flue Agents to Cloudflare Workers: Production Considerations
Learn how to deploy Flue agents to Cloudflare Workers, focusing on production considerations like scalability, state management, and the `wrangler` CLI.
- Build Robust Flue Agents for Scalability and Maintenance
Design production-ready Flue agents using architectural best practices, advanced state management, error handling, and observability for reliable AI products.
- Reduce Context Switching with omp.sh AI Terminal Assistant
Install and configure omp.sh, an AI terminal assistant, to reduce context switching and boost efficiency in your daily coding workflow.
- Install omp.sh and Configure AI Provider API Keys
You will install the omp.sh AI coding agent and securely configure its essential AI provider API keys to enable AI assistance.
- Strategic Problem-Solving: Leveraging Plan Mode and Goal Mode
Master strategic problem-solving with omp.sh's Plan Mode and Goal Mode, guiding AI agents through complex coding tasks with structured plans and iterative goal-setting.
- Your First AI-Powered Coding Steps: Core Agent Commands
Discover the power of omp.sh by learning its core commands and how to integrate AI directly into your terminal-based coding workflow for enhanced productivity.
- Collaborative Intelligence: Subagents and Hindsight Memory
Explore how omp.sh leverages collaborative intelligence through subagent-like workflows and learns from past interactions with hindsight memory, enhancing problem-solving in your terminal.
- `omp.sh` AI Agents: Precise Edits & Code Context with LSP/DAP
Learn how `omp.sh` ensures AI agents make precise code changes with Hashline Edits and gain deep contextual understanding through LSP/DAP integration.
- Integrate omp.sh AI Agent into Your Dev Workflow
Readers will learn to integrate the omp.sh AI agent into their development workflow, configuring AI providers and leveraging advanced features to solve complex coding tasks.
- Mastering omp.sh Best Practices and AI Agent Comparison
Discover omp.sh best practices, understand its limitations, and compare it to other AI coding agents to optimize your development workflow.
- Building Kanbots: AI Agents, Git Worktrees, and Desktop Automation
Build Kanbots, a desktop Kanban app orchestrating AI agents on cards using Git worktrees for isolated tasks. Learn multi-persona development workflows with Tauri and Svelte.
- Mastering GPUI: A Deep Dive Guide
Dive deep into GPUI, Zed's UI framework, from environment setup to building complex AI chat agents. Learn core concepts, unstable APIs, and best practices directly from source.
- Setting Up Your Kanbots Workshop: Tauri v2 and Svelte 5
Kickstart your Kanbots project by setting up the foundational Tauri v2 desktop framework with a Svelte 5 frontend, creating a robust environment for AI-powered workflows.
- Integrate AI Agents in Kanbots to Automate Code Generation
Learn to integrate AI agents into Kanbots, enabling them to generate code and perform tasks within isolated git worktrees directly from Kanban cards.
- Mastering Git Worktrees for Isolated Agent Tasks
Learn how to leverage Git worktrees within Kanbots to provide isolated development environments for AI agents, enabling parallel and conflict-free task execution.
- Orchestrating Multi-Agent Workflows with Personas
Learn to orchestrate multi-agent AI workflows in Kanbots, assigning distinct personas for tasks like code generation and review using Git worktrees.
- Real-time Agent Progress and User Control UI
Equip Kanbots with real-time AI agent progress, logging, and user controls like pause, resume, and cancel, enhancing user interaction with multi-agent workflows.
- Logging Agent Activities and Deployment Considerations
Implement robust logging for AI agent activities within Kanbots and understand the crucial steps for packaging and deploying your cross-platform desktop application.
- Kanbots: AI Agents, Worktrees, & Dev Workflows
Master Kanbots: integrate AI agents like Claude/Codex, leverage git worktrees for isolated runs, and orchestrate multi-agent dev workflows with practical persona-based examples.
- Reshaping Software Engineering with Autonomous AI Agents
Understand how autonomous AI agents are reshaping software engineering workflows, offering efficiency gains and new challenges for developers.
- Building Persistent AI Agents with Google ADK: Pause, Resume, Recover
Learn to build robust, long-running AI agents using Google ADK, capable of persisting state and context, allowing for pause, resume, and recovery.
- Build a Stateless AI Agent with Google ADK
You will learn to set up and run a basic, stateless AI agent with Google's Agent Development Kit, enabling responses to simple prompts.
- Enhancing Agent Intelligence with Tools and Multi-Step Workflows
Equip your ADK agent with external tools and orchestrate complex, multi-step workflows, leveraging persistent state for robust, intelligent behavior.
- Building Persistent ADK AI Agents
Master building production-ready long-running AI agents using ADK. Learn architectural design, implementation phases, and robust strategies for state and context persistence.
- Trigger.dev: A Zero-to-Advanced Guide for AI Workflows
Embark on a comprehensive journey to master Trigger.dev v4-beta, learning to build, deploy, and manage robust AI agents and automated workflows for modern production systems.
- Welcome to Trigger.dev v4-beta: The Foundation for Modern Workflows
Dive into Trigger.dev v4-beta, understanding its core concepts, setting up your first project, and building robust, scalable workflows for AI applications and backend automation.
- Setting Up Your Trigger.dev Environment & First Workflow
Learn how to set up your Trigger.dev v4-beta environment, initialize a new project, and create your very first durable background job with step-by-step instructions and practical examples.
- Unleashing AI Agents: Building Smart, Automated Systems
Discover how to build, deploy, and manage intelligent AI agents and automated workflows using Trigger.dev v4-beta, integrating tools and human-in-the-loop processes.
- Human-in-the-Loop & Real-time Updates: Collaborative Workflows
Learn how to integrate human decisions and real-time feedback into robust, durable Trigger.dev workflows for AI agents and collaborative systems.
- Build Custom Connectors for Trigger.dev and the MCP
Build custom Trigger.dev connectors and utilize the Managed Connector Platform to integrate with any external service, enhancing your AI workflows.
- Real-World Project: Building an AI-Powered Customer Support Agent
Learn to build an AI-powered customer support agent using Trigger.dev, integrating AI, human escalation, and durable workflows for robust, real-world applications.
- AIPack: Building Production-Ready AI Agents
Learn to build, run, and share robust AI agents for production workflows using AIPack, covering architecture, multi-stage agents, Lua logic, and VS Code integration.
- Welcome to AIPack: Your Agentic Runtime for AI
Discover AIPack, an open-source agentic runtime for building, running, and sharing AI agents. Learn installation, core concepts, and create your first agent.
- Set Up Your AIPack AI Agent Development Environment
Readers will learn to configure their system with Python, AIPack CLI, Ollama, and VS Code, enabling efficient development of AI agents.
- Your First AI Pack: Understanding .aip Files and Basic Agents
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 integrate basic Lua logic for control flow.
- Building Multi-Stage Markdown Agents for Complex Workflows
Learn how to design and implement multi-stage markdown agents using AIPack, leveraging Lua for dynamic control flow and building robust AI workflows.
- Connecting to AI: Provider Integrations (Ollama, Cloud APIs)
Learn how to connect your AIPack agents to various AI models, including local setups with Ollama and popular cloud provider APIs, for powerful agentic workflows.
- Streamline AI Agent Development with VS Code and MCP
Efficiently develop AI agents by integrating Visual Studio Code and the Multi-Agent Communication Protocol for streamlined debugging and improved workflows.
- Agent Composition and Reusable Skills: Building Modular Agents
Explore how to build complex AI agents by composing smaller, specialized agents and creating reusable skills with AIPack's modular architecture.
- Real-World Project: AI-Assisted Python Debugging Agent
Build an AI-assisted Python debugging agent with AIPack. Learn to integrate AI into your debugging workflow, leveraging MCP and multi-stage agents to identify and propose fixes for Python errors.
- Debugging, Optimization, and Production Readiness for AI Packs
Master debugging, optimizing, and preparing your AIPack AI agents for reliable, cost-effective production deployment. Learn about MCP server insights, prompt engineering, and robust error handling.
- Best Practices for Building and Sharing Production AI Packs
Master best practices for designing, developing, debugging, and sharing robust, production-ready AI Packs using AIPack, focusing on modularity, context management, and effective tooling.
- AIPack Zero-to-Mastery Guide
Master AIPack from installation to production. Learn architecture, multi-stage agents, Lua logic, local/cloud models, VSCode workflows, and real-world AI-assisted engineering.
- Choosing Synchronous or Asynchronous Service Communication
Learn to choose between synchronous and asynchronous communication patterns in distributed systems to build scalable, resilient, and performant applications.
- Build Reactive & Scalable Systems with Event-Driven Architectures
Understand event-driven architecture fundamentals and practical patterns to design and build reactive, scalable, and resilient systems that adapt to change.
- Introduction to Edge AI Agents and Environment Setup
Understand the landscape of on-device AI agents and tiny LLM systems, set up your development environment, and explore core tooling for edge AI.
- Build On-Device AI Agent Intent Mapping with Local LLMs
Learn to build a Python pipeline that uses a local LLM to convert transcribed text into structured user intents and entities for on-device AI.
- Edge AI Agent & Tiny LLM Projects for On-Device Apps
Build intelligent, autonomous AI agent and tiny LLM applications directly on edge hardware using modern edge AI tooling and frameworks.
- Hermes Agent vs OpenClaw: Complete Comparison 2026
Comprehensive comparison of Hermes Agent and OpenClaw - features, performance, pros & cons, and when to use each for open-source AI agent systems.
- Prompt Engineering and Agentic AI for Production
Learn to build and deploy advanced AI applications using prompt engineering and agentic AI workflows, focusing on practical, production-ready techniques for developers.
- Structure LLM Prompts for Precision and Reliability
Learn to craft secure and reliable prompts using system messages, delimiters, and structured output control for predictable LLM interactions.
- Advanced Reasoning with Chain-of-Thought and Self-Consistency
Unlock robust LLM reasoning with Chain-of-Thought and Self-Consistency. Learn to guide LLMs through complex problems, improving accuracy and reliability in production AI applications.
- Developing Robust Agents: Design Patterns for Production Readiness
Dive into advanced design patterns for building robust, scalable, and reliable AI agents ready for production environments.
- Agentic AI: LLM, Memory, Tools, Planning Explained
Learn to deconstruct Agentic AI systems by understanding how LLMs, memory, tools, and planning combine to create dynamic, problem-solving agents.
- Empowering Agents with Custom Tools and API Integrations
Extend your AI agents' capabilities by integrating custom tools and external APIs to access real-time data and perform actions beyond their core LLM knowledge.
- Evaluate & Test AI Prompts & Agents for Performance
Learn to rigorously evaluate and test your prompts and AI agents for accuracy, reliability, cost-efficiency, and safety in production environments.
- Orchestrating Agents with Frameworks: LangChain and LlamaIndex
Learn how to orchestrate complex AI agents using popular frameworks like LangChain and LlamaIndex, integrating LLMs, tools, and memory for production-ready applications.
- Introduction to Retrieval-Augmented Generation (RAG)
Grasp Retrieval-Augmented Generation fundamentals, see its value for production LLM applications, and build a functional RAG system using Python.
- Build AI Agents with Persistent Short-Term and Long-Term Memory
Learn to equip AI agents with persistent short-term context and long-term knowledge, making them more capable, consistent, and useful in applications.
- Production Deployment: Scaling, Cost Optimization, and Ethical AI
Take your AI agents from prototype to production. Learn critical strategies for scaling, optimizing costs, and ensuring ethical and responsible deployment of your agentic AI applications.
- Mastering Production Prompt Engineering & Agentic AI
Master prompt engineering & agentic AI for developers. This 2026 guide focuses on real-world production workflows, taking you from beginner to mastery.
- Agentic AI Systems: A Comprehensive Guide
Explore the principles and practical applications of Agentic AI Systems, covering autonomous agents, planning, reasoning, tool usage, memory, and multi-agent coordination.
- Design and Build AI Agents for Complex Workflows
Discover how to build advanced AI applications that manage complex, multi-step tasks, maintain context, and seamlessly integrate with various external tools.
- Understanding AI Agent Memory Systems: A Practical Guide
Explore the essential role of memory in AI agents, covering different memory types, storage, retrieval, and how agents use them to learn and maintain context.
- Building Advanced AI Systems with Agents and Orchestration
Learn to design, build, and manage complex, collaborative AI systems using advanced engineering principles like agents and orchestration.
- AI Security for LLMs and Agentic Applications
Secure AI systems, including LLMs and agentic applications, by understanding and mitigating prompt injection, data poisoning, and critical design flaws.
- CLI-First AI Systems: Terminal Agents and Automation
Learn to integrate AI agents directly into your terminal workflows, automating command-line tasks, enhancing developer processes, and orchestrating multi-agent systems.
- Integrate AI Agents with Tools using Model Context Protocol (MCP)
Learn to integrate AI agents with external tools using the Model Context Protocol, covering tool schemas, registration, and practical applications.
- Modern AI Engineering: Core Concepts & Emerging Topics (2026)
A structured overview of the most important and trending AI engineering topics in 2026, covering agent systems, context engineering, infrastructure, and modern AI workflows.
- Advanced RAG Techniques for Building Intelligent AI Systems
Learn to implement advanced Retrieval-Augmented Generation techniques like hybrid search and GraphRAG to build highly accurate and robust AI applications.
- Equip AI Agents with Long-Term Memory Using Agentic RAG
You will build a Retrieval-Augmented Generation system using vector databases, enabling AI agents to access external knowledge and reduce hallucinations.
- Develop Robust AI Agents with ReAct, Reflection, and Iteration
Learn to design and implement advanced AI agent architectures like ReAct and Reflection to create self-correcting, robust, and adaptable systems.
- AI Agent Reasoning: Planning, Problem-Solving, and ReAct Techniques
You will learn how AI agents plan, solve problems, and make intelligent decisions using advanced reasoning patterns like ReAct and reflection.
- Short-Term Recall: Managing Agent Context and Conversation Memory
Explore short-term memory in Agentic AI systems, focusing on LLM context windows, conversation history management, and practical Python implementations for building coherent, stateful agents.
- How Agents Think: Designing Planning and Task Decomposition
Dive deep into the planning and task decomposition mechanisms that enable autonomous AI agents to break down complex goals into manageable steps. Learn how agents think and strategize.
- Equip AI Agents with External Tools for Real-World Action
Learn to equip autonomous AI agents with external tools and APIs to interact with the real world, perform actions, and overcome LLM limitations effectively.
- The Future of Agentic AI: Ethical Considerations and Control
Explore the critical ethical considerations and robust control mechanisms essential for designing, deploying, and managing autonomous AI agents safely and responsibly.
- Building Your First Agent: A Hands-On Autonomous System Project
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 assistant.
- Agentic AI Agents: Understanding Autonomous AI Systems
After reading, you will understand what autonomous AI agents are, their core components, and how they enable systems to think, plan, and adapt.
- Powering AI Agents with Large Language Models
Learn to integrate Large Language Models into AI agents, enabling them to reason, plan, and make decisions through effective API interactions.
- Production-Ready Agents: Best Practices, Pitfalls, and Deployment
Learn how to design, deploy, and manage production-ready autonomous AI agents, covering best practices for robustness, security, scalability, and ethical considerations.
- Designing and Orchestrating Multi-Agent AI Systems
Design, coordinate, and orchestrate multi-agent AI systems effectively to solve complex problems and achieve robust, collaborative intelligence.
- Agentic AI Systems: A 2026 Guide
Explore Agentic AI Systems in 2026, covering autonomous agent architectures like ReAct, planning, memory, multi-agent coordination, and real-world applications.
- Advanced Tooling for AI Agents and External API Integration
Learn to build robust tools for AI agents, integrating external APIs, managing complex data flows, and handling errors to enable real-world interactions.
- AutoGen: Crafting Conversational and Collaborative Agent Teams
Dive into AutoGen, Microsoft's framework for building multi-agent systems that collaborate through conversational AI. Learn to define agent roles, enable tool usage, and orchestrate complex workflows.
- Understand LLMs, Tools, and Memory for AI Agent Development
Learn how Large Language Models, external tools, and memory function as the essential building blocks for creating intelligent and adaptable AI agents.
- CrewAI: Empowering Agents with Roles, Tasks, and Collective Goals
Explore CrewAI, a powerful framework for orchestrating role-playing, autonomous AI agents to achieve complex collective goals through structured tasks and collaboration.
- Debugging, Testing, and Monitoring: Building Reliable Agent Systems
Master debugging, testing, and monitoring strategies for AI agent systems built with LangGraph, AutoGen, CrewAI, and Semantic Kernel to ensure reliability and performance.
- Choosing the Right AI Agent Framework for Your Projects
You will learn to evaluate leading AI agent frameworks like LangGraph, AutoGen, and CrewAI, enabling you to choose the best fit for your multi-agent applications.
- LangGraph: Building State Machines for Dynamic Agent Workflows
Dive into LangGraph to build dynamic, stateful AI agent workflows. Learn about state machines, graph nodes, and edges for complex agent orchestration with practical Python examples.
- Architect Multi-Step AI Agent Workflows with Patterns
Learn to design robust multi-step AI agent workflows by structuring interactions, managing state, and orchestrating communication across diverse patterns.
- Persistent Memory & Context Management: Remembering the Past
Explore how AI agent frameworks manage short-term and long-term memory, and track workflow state to build intelligent, conversational, and persistent applications.
- Project: Building an Automated Financial Analysis Assistant
Build an automated financial analysis assistant using CrewAI. Learn to define agents, integrate tools, manage tasks, and orchestrate a multi-step workflow for generating investment insights.
- Semantic Kernel: Skills, Planners, and Enterprise AI Integration
Explore Semantic Kernel's architecture, including Skills and Planners, for building robust enterprise AI applications with Python.
- Unveiling AI Agents: The Next Frontier in Application Development
Discover the foundational concepts of AI agents, their architecture, and why they represent a paradigm shift in building intelligent applications beyond simple LLM prompts.
- Mastering Modern AI Agent Frameworks
Explore leading AI agent frameworks like LangGraph, AutoGen, CrewAI, and Semantic Kernel. Master multi-step workflows, memory, and tool orchestration for complex AI applications.
- Working, Short-term, and Long-term Memory in AI Agents
Learn how Working, Short-term, and Long-term Memory enable AI agents to maintain context, learn, and overcome LLM limitations in practical designs.
- Advanced Concepts & Best Practices for Production-Ready Memory Systems
Explore advanced concepts and best practices for designing and implementing robust, scalable, and secure memory systems for AI agents in production environments.
- Build a RAG Agent with Memory for Contextual AI Responses
You will learn to build a RAG agent that uses memory and conversational history to provide accurate, context-aware AI responses.
- Introduction to AI Agent Memory: Why Agents Need to Remember
Explore the fundamental need for memory in AI agents, understanding how it overcomes LLM limitations and enables more intelligent, stateful, and personalized interactions.
- AI Agent Long-Term Memory: Episodic & Semantic Foundations
Readers will understand how AI agents utilize episodic and semantic memory to store past experiences and general knowledge, enabling complex, adaptive behaviors.
- Vector Memory and Embeddings: The Power of Similarity
Explore vector memory and embeddings, understanding how AI agents leverage numerical representations for efficient similarity-based information retrieval and enhanced contextual understanding.
- Storing Agent Memories: From Files to Databases and Vector Stores
Explore how AI agents store their memories, from simple file systems to advanced vector databases, understanding the trade-offs and practical implementations.
- Retrieving AI Agent Memories for Contextual Awareness
Learn how AI agents retrieve information from memory using various strategies to achieve contextual awareness and overcome LLM limitations.
- AI Agent Memory Systems Explained
Explore AI agent memory systems: vector, semantic, episodic, and long-term. Understand storage, retrieval, and memory-context trade-offs in agent design.
- Advanced Architectures for Multi-Agent AI Systems
Learn to design and build robust, scalable, and intelligent multi-agent AI systems using advanced architectures and orchestration patterns.
- Building AI Agent Systems Beyond Single Models
You will grasp fundamental principles for designing and building robust, scalable AI systems, moving from single models to multi-agent architectures.
- Agent Operating Systems for Building AI Agents
You will learn how Agent Operating Systems provide the foundational infrastructure for managing and orchestrating intelligent, autonomous AI agents.
- AI Orchestration Engines: Harmonizing Multi-Agent Collaboration
Explore AI Orchestration Engines, their role in coordinating multi-agent systems, key components, and practical patterns for building complex, collaborative AI solutions.
- AI Workflow Languages: Defining Intelligent Task Flows
Dive into AI Workflow Languages, understanding how they define, manage, and execute complex sequences of AI models, tools, and logic for robust intelligent systems.
- Dissecting AI Agents: Core Components and Capabilities
Explore the fundamental building blocks of AI agents: perception, memory, planning, tool use, and communication. Understand how these core components enable intelligent behavior in complex systems.
- Future Trends and Ethical Development in AI Engineering
Understand future AI engineering trends, including specialized agents, and apply ethical principles for responsible, beneficial AI development.
- Hands-On Project: Building a Collaborative AI Assistant
Build a collaborative AI assistant using multi-agent principles, leveraging tools and orchestration to solve complex problems.
- Tool Marketplaces: Empowering Agents with External Abilities
Explore AI Tool Marketplaces, how they empower AI agents with external capabilities, and their role in modern AI orchestration and development.
- Testing, Evaluating, and Observing AI Agents for Reliability
Learn to test, evaluate, and observe AI agents and multi-agent systems to ensure their reliability, manage emergent behaviors, and maintain performance.
- Prevent Tool Misuse and Insecure Outputs in Agentic AI
Learn to protect agentic AI systems from tool misuse and ensure safe output handling, building robust defenses against critical vulnerabilities.
- Build Live Runtime Defenses for Secure AI Agents in Production
Learn to implement active runtime protection for AI agents, covering real-time monitoring, access control, and threat response for secure production.
- Secure LLM and Agent Systems with AI Threat Modeling
Apply AI threat modeling frameworks to proactively identify and mitigate security threats in LLMs and agentic applications, building robust and secure systems.
- AI Security Fundamentals: Protecting LLMs and Autonomous Agents
Learn to identify and mitigate security risks in AI systems, focusing on threats to LLMs and autonomous agents, and implement robust defense strategies.
- Building a Secure LLM Interaction Layer in Python
After this, you can build a secure Python interaction layer for LLMs, protecting against prompt injection and insecure outputs with defense-in-depth.
- Prompt Injection: The Art of Manipulation (Direct & Indirect)
Uncover the critical threat of Prompt Injection, the #1 vulnerability in LLM applications. Learn about direct and indirect attacks and initial defense strategies to secure your AI systems.
- OWASP Top 10 for LLMs: Guarding Against AI Application Threats
Understand the OWASP Top 10 for LLM applications, identifying critical vulnerabilities and applying strategies to build secure AI systems.
- Architecting AI Systems with LLMs, Generative AI, and Agents
Learn to design and integrate scalable, trustworthy AI systems that leverage Large Language Models, Generative AI, and multi-agent orchestration patterns.
- Orchestrating Complex AI Workflows and Multi-Agent Systems
Learn how to design and implement robust orchestration for complex AI workflows and multi-agent systems, enhancing scalability and reliability.
- AI as Your Debugging Partner: Error Analysis and Fix Suggestions
Master AI-powered debugging with GitHub Copilot and Cursor 2.6. Learn to analyze errors, get fix suggestions, and leverage AI agents for efficient troubleshooting in Python.
- AI Agents and Automations to Streamline Dev Workflows
Learn to leverage AI agents and automations for proactive task management and streamlined software development workflows.
- Automate Pull Requests with Multi-Agent AI Workflows
Learn to orchestrate multi-agent AI systems for complex development tasks, automating the pull request lifecycle and boosting developer productivity.
- Setting Up Cursor 2.6 and GitHub Copilot for AI Coding
Learn to set up and configure Cursor 2.6 and GitHub Copilot, covering prerequisites, installation, and initial settings to enhance your AI-powered coding workflow.
- Welcome to AI-Augmented Development: Copilots vs. Agents
Explore the transformative world of AI-augmented development, differentiating between interactive AI copilots and autonomous AI agent systems, and understand their roles in modern coding workflows.
- AI Agents for Smarter Development, Debugging & Scripting
Learn to integrate AI agents into your development workflow to automate commands, create dynamic scripts, and enhance debugging in your terminal.
- Mastering CLI-First AI: Best Practices, Security, and Future Trends
Explore best practices for designing and deploying CLI-first AI agents, understand critical security considerations, and envision the future trends and ethical implications of AI in the terminal.
- Beyond Chat: Automating Terminal Tasks with AI Agents
Move beyond conversational AI to automate complex terminal tasks with AI agents. Learn about command generation, shell tool integration, and AI-discoverable skills.
- Talking to AI: Your First Steps with a CLI Agent (e.g., Gemini CLI)
Take your first steps with a CLI-first AI agent like Gemini CLI. Learn to install, configure, and interact with AI directly from your terminal for command generation and task automation.
- Connect AI Agents to Shell Tools for Powerful CLI Automation
Learn to integrate AI agents with your existing shell tools to create powerful command-line automation and enhance your developer workflows.
- CLI-First AI Systems: A Developer's Guide
Explore CLI-first AI systems, learning how AI agents integrate with terminal environments for automation, scripting, and enhanced developer workflows. Discover practical examples.
- Orchestrate Multi-Agent AI Workflows and Discoverable CLI Skills
Learn to coordinate multiple AI agents for complex tasks and define AI-discoverable skills that empower them to utilize existing command-line tools effectively.
- Get Started with CLI-First AI Agents in Your Terminal
Learn to integrate AI agents directly into your terminal to automate commands, streamline scripting, and enhance developer workflows with practical examples.
- Invoke External Tools with LangChain.js AI Agents and MCP
Integrate custom Model Context Protocol tools into LangChain.js AI agents to enable them to query information and interact with external services.
- Understand Model Context Protocol for AI Agent Tool Integration
After reading, you will understand how the Model Context Protocol enables AI agents to discover and interact with external tools and data.
- Building Full MCP Applications: UI, Async, Security
Readers will learn to design, implement, and secure Model Context Protocol applications, integrating UI resources, async patterns, and secure deployment.
- Orchestrating MCP Tool Execution & Request Routing
Learn how Model Context Protocol orchestrates tool execution and request routing, empowering AI agents to interact with external tools effectively.
- Register and Discover AI Agent Tools Using the MCP
After this chapter, you will register custom MCP tools and understand how AI agents discover and utilize them, including UI resources.
- Security Best Practices for AI Agent Integrations with MCP
Implement robust permissions, authorization, and security best practices to protect AI agent integrations and sensitive data within the MCP ecosystem.
- Set Up Your MCP Development Environment with TypeScript SDK v2
Configure your local development environment for the Model Context Protocol by installing Node.js, TypeScript, and the MCP TypeScript SDK v2.
- Integrate AI Tools for Agents Using Model Context Protocol (MCP)
Learn to define, register, and utilize AI tools with the Model Context Protocol, covering schemas, execution, routing, permissions, and security.
- Crafting MCP Tool Schemas for AI Agent Capabilities & UI
Learn to craft robust MCP tool schemas using TypeScript and JSON Schema, empowering AI agents to understand and effectively utilize custom tools.
- LLM Planning for Agentic Retrieval in RAG 2.0
Learn to implement agentic retrieval systems where LLMs plan and execute complex information retrieval tasks, advancing your RAG 2.0 capabilities.
- Akka Agentic AI vs LangChain: Complete Comparison 2026
Comprehensive comparison of Akka Agentic AI and LangChain - features, performance, pros & cons, and when to use each for LLM orchestration and agentic AI development.
- Integrating AI & Agentic Features
Explore integrating on-device AI with Core ML, leveraging cloud-based AI APIs, and designing agentic features for intelligent user interactions in iOS apps using modern Swift 6 and Xcode 16+.
- Build Collaborative Customer Service with Multi-Agent AI
Learn to design and implement robust collaborative customer service workflows by orchestrating multiple specialized AI agents with an open-source SDK.
- Building a Real-World Customer Support Agent (Project 1)
Learn to build a multi-agent customer support system using OpenAI's Agents SDK, covering agent design, implementation, and testing.
- Enterprise AI Agents: Strategic Impact on Business & Workforce
Learn how AI agents strategically reshape enterprise business models, workforce dynamics, and competitive advantage for future success.
- AI & Agentic AI in React & React Native Frontend
Learn to integrate AI and agentic AI into React and React Native frontend applications for smarter, more engaging user experiences.
- Streaming Intelligence: Real-time UI Updates
Learn how to implement real-time UI updates with streaming intelligence in your frontend applications.
- Empowering Agents: UI-Driven Tool Calling
Learn how to integrate UI-driven tool calling into your React and React Native applications to empower AI agents.
- Orchestrating Intelligence: Client-Side Agents & State
Learn how to build UIs that collaborate with AI agents, managing their state and interactions.
- Project: Agent-Driven UI Workflow for Task Automation
Learn how to build an agent-driven UI workflow for task automation in React or React Native.
- Your First Kiro Agent: A Guided Tour
Learn to configure, deploy, and interact with your first Kiro agent for enhanced development workflow.
- The Model Context Protocol (MCP)
Learn about the Model Context Protocol (MCP) in AWS Kiro, its role in facilitating communication and context sharing among AI agents.
- Testing Strategies for Kiro Agents
Learn how to effectively test AWS Kiro agents for correctness, consistency, and reliability.
- Advanced Prompt Engineering with Kiro
Learn advanced prompt engineering techniques for Kiro, the intelligent AI agent for AWS.
- CI/CD Pipelines with AWS Kiro
Learn how to integrate AWS Kiro into your CI/CD pipelines for automated code reviews and more.
- Debugging and Troubleshooting Kiro Agents
Learn how to debug and troubleshoot Kiro agents, including understanding logs, MCP insights, and AWS CloudWatch.
- Project: Enhancing a Web Application with Kiro Agents
Learn how to use AWS Kiro to enhance a Python Flask web application with agent-driven development.
- Performance Tuning and Optimization for Kiro
Learn how to optimize AWS Kiro for better performance, cost-effectiveness, and smarter AI solutions.
- Applied & Agentic AI: Build Intelligent Agents & Systems
Master the skills to design, build, and deploy sophisticated Applied and Agentic AI systems, creating intelligent agents for practical, real-world solutions.
- Foundations of Applied AI: Python & System Thinking
Learn the fundamentals of Applied AI with Python, setting up your environment, and cultivating system thinking.
- Understanding Large Language Models (LLMs) & AI APIs
Learn how to interact with Large Language Models using AI APIs in Python, setting the foundation for building intelligent applications.
- Mastering Prompt Engineering for Effective LLM Instruction
Learn the principles and practices of crafting effective prompts to guide Large Language Models for specific tasks and build intelligent AI agents.
- Tool Use & Function Calling: Extending LLM Capabilities
Learn how to extend LLM capabilities with tools and function calling for real-world applications.
- Enhance LLMs with Custom Data using Retrieval-Augmented Generation
After this chapter, you will implement a RAG system to enhance large language models with external, up-to-date, and domain-specific knowledge.
- Introduction to AI Agents: Autonomy in Action
Learn to build and understand AI agents that perceive, reason, and act autonomously using Python and LLMs.
- Designing AI-Driven Workflows & Complex Agent Patterns
Learn how to design and orchestrate complex AI-driven workflows using multi-agent systems.
- Evaluation, Observability & Debugging AI Agents
Learn how to evaluate, observe, and debug AI agents for better performance and reliability.
- Agent Orchestration & Multi-Agent Systems
Learn how to design, build, and coordinate multiple AI agents for complex tasks using agent orchestration.
- Cost and Latency Optimization for Production AI
Optimize AI solution costs and latency by applying techniques for token management, model selection, caching, and concurrent processing for production.
- Security, Privacy & Ethical AI Development
Learn about the unique security threats, privacy concerns, and ethical considerations in developing agentic AI systems using LLMs.
- Build a Smart AI Research Assistant Agent Project
Design and implement a multi-agent AI system to build a smart research assistant that searches, synthesizes, and summarizes information.
- Build Collaborative Multi-Agent Systems with Python & LLMs
Build a collaborative multi-agent system using Python and LLMs, applying collective intelligence to solve complex problems effectively.
- Build an Autonomous AI Workflow Agent Project
You will design and implement an autonomous workflow agent, orchestrating a multi-agent system to intelligently plan, execute, and collaborate on complex tasks.
- Conclusion
Comprehensive overview of agentic AI patterns and their role in building intelligent systems.
- Build Interactive A2UI: Actions and State Management
Master how to implement interactivity, handle user actions, and manage UI state within agent-driven A2UI interfaces, enabling your AI agent to respond dynamically.
- Basic Agent Integration - Generating Static UI
Learn how to integrate an AI agent with A2UI to generate static user interfaces using the Python ADK.
- Advanced Agent Architectures and A2UI Orchestration
Learn how to build and orchestrate multi-agent systems using A2UI for intelligent, interactive applications.
- Best Practices for A2UI Development
Learn best practices for developing agent-driven UIs with A2UI, focusing on declarative generation and alignment.
- Develop and Optimize AI Agents with Agentic Lightening
Gain practical skills with Microsoft's Agentic Lightening, from environment setup and core concepts to building and optimizing advanced AI agent projects.
- Core Concepts: Agents, Trainers, and the Lightning Server
Explains the core concepts of Agentic Lightening, including LitAgent, AgentLightningServer, Trainer, and LightningStore.
- Introduction to Agentic Lightening
An introduction to Agentic Lightening, an open-source framework for optimizing AI agents with minimal code changes.
- Project 2: Enhancing a LangChain Agent with Reinforcement Learning
Learn how to enhance a LangChain agent with Reinforcement Learning using Agentic Lightening for better decision-making and tool usage in multi-step problems.
- Build & Optimize a QA Agent with Automatic Prompt Optimization
Readers will learn to build and optimize a question-answering agent, enhancing its accuracy by dynamically tuning prompts with Agentic Lightening's APO.
- Building AI Agents in Java with Spring Boot: A Comprehensive Guide
Learn how to build intelligent AI agents using Java and Spring Boot with practical examples.
- Building AI Agents in Java with Spring Boot: A Comprehensive Guide
Learn to build intelligent AI agents using Java and Spring Boot with practical examples.
- Build Production-Ready Agentic AI for UI and Backend
Developers will learn to design, deploy, and manage advanced production-ready agentic AI systems for both user interface and backend applications effectively.
- Mastering Advanced Agentic AI for Production UI & Backend
Master the intricacies of building, deploying, and managing advanced agentic AI systems for production UI and backend applications.