#AI Agent
1114 related articles
Why Is AI Agent the Next Big Thing? From Digital Employees to Enterprise Deployment
AI Agents are becoming the new frontier for enterprise efficiency. Explore how Agents differ from chatbots, real deployment scenarios, and what skills the job market actually demands.
From Function Calling to MCP and Agents: How LLM Tool Calling Really Works
A deep dive into how LLM Function Calling works across 5 steps, with a hands-on weather query demo, and how it underpins MCP and Agent development.
AI Agent Development Learning Roadmap: A Four-Stage Guide from Concepts to Real-World Practice
A four-stage AI Agent development roadmap: from core concepts and ReAct/CoT paradigms to prompt tuning, multi-agent collaboration, and real-world project delivery.
AI Agent Development in Practice: A Complete Breakdown of the LangChain + MCP + RAG Stack
A deep dive into AI Agent development with LangChain, LangGraph, MCP, and RAG. Includes a hands-on Vue project case study illustrating the perceive-decide-act loop.
Agent Engineer Learning Roadmap: From Zero to Production-Ready Deployment
A complete Agent engineer learning roadmap covering LLM fundamentals, LangGraph, MCP/A2A protocols, context engineering, GraphRAG, and Vibe Coding — with 100k-line production projects.
Agent Developer: The High-Paying AI Role No One Can Fill — And How to Break In
Agent Developers are the hardest-to-hire, highest-paid new AI roles. Learn what they do, why they're scarce, and a 4-step path from prompt engineering to Agent workflows.
AI Agent Explained: From Core Concepts to Key Components
Learn the differences between Agents, chatbots, and LLMs, plus the three core components of AI Agents, agent categories, and AI's evolution from machine learning to generative AI.
AI Agent Terminology Too Confusing? One Interactive Concept Map to Untangle 40+ Core Terms
Confused by AI Agent terms like MCP, harness, orchestration, and skills? AI Concept Atlas is an interactive map visualizing 40+ concepts and their relationships, with cited sources.
Omarion SEC CLI: How a Self-Healing Autonomous Agent Tackles AutoGPT's Persistent Problems
Omarion SEC CLI is an open-source autonomous CLI agent that solves AutoGPT's error loops, terminal clutter, and session amnesia through execution fingerprinting, intent routing, and persistent memory.
No Code Required: Build Your First AI Agent with the Microsoft Foundry Portal
Build a production-ready AI agent without writing code: deploy models, create agents, connect MCP tools, inspect traces, and run evaluations — all visually in Microsoft Foundry.
MCP Event-Driven Extension: Freeing AI Agents from the Chat Window
MCP core maintainer explains the experimental Triggers and Events extension, enabling AI Agents to be driven by GitHub, monitoring, and message queue events via Pull, Push, and Webhook delivery modes.
MCP 2.0 Deep Dive: How a Stateless Protocol Is Reshaping the AI Agent Communication Standard
A deep dive into MCP 2.0 (Model Context Protocol): the shift from stateful to stateless, the extensions mechanism, governance maturity, and the agentic messaging primitives roadmap.
GitHub MCP in Practice: New Protocol, Elicitation, and Stateless Servers
A GitHub engineer shares firsthand MCP experience across server, client, and protocol spec: new spec rollout, MRTR enabling elicitation on stateless servers, and Skills over MCP and Server Cards.
Hermes Agent Hands-On: The Open-Source AI Assistant That Learns and Evolves
Hermes Agent is a self-evolving open-source AI assistant. Learn how to deploy it on a VPS, connect it to Telegram, and turn workflows into reusable skills.
The Truth Behind Claude's Challenge Loop: How Wayfinder Skills Let AI Build Real Apps in One Shot
How Claude's Challenge Loop works, its two critical flaws, and how Matt Pocock's Wayfinder skill generates verifiable spec files so AI agents can build real apps — not just game demos — in one shot.
Hands-On with Open-Source Desktop Client CC-HAHA: Let AI Automatically Control Your Computer
CC-HAHA is an open-source desktop client with computer use support, letting AI control your GUI via virtual mouse. Learn the 3-step setup and model comparisons.
What Should You Actually Learn About AI Agents? A Cognitive Map to Clarify the Core Concepts
What should you learn about AI agents? This article uses a cognitive map — not an architecture diagram — to clarify prompts, RAG, workflows, MCP, and multi-agent systems for non-engineers.
AI Agent Beginner's Guide: Core Architecture and How Intelligent Agents Work
A complete beginner's guide to AI Agents: covers the definition, large model evolution, perception/brain/action architecture, and memory mechanisms like RAG and System Prompt.
Enterprise AI Agent in Practice: A Dual-Track Strategy with Low-Code and Hardcore Frameworks
A dual-track enterprise AI Agent framework: low-code platforms (Coze/Dify/n8n) and code frameworks (LangChain/LangGraph/CrewAI), covering MCP protocol, nine smart job roles, and four real-world projects.
How to Test AI Agent Policies Before Going Live: Blocking Risks Without False Positives
How should AI Agent policies be tested before enforcement? This article covers intent recognition, RBAC, and approvals, plus shadow mode and WorkOS Airlock for safe deployment.