#AI Agent
1074 related articles
AI Agents Aren't Chat Boxes: Breaking Down the Plan-Tool-Read-Decide Loop
An AI Agent isn't a smarter chatbot but a four-step loop: plan, call tools, read results, decide. This breakdown uses an 18KB file task to explain the mechanism, compares Claude Code, Codex, and ChatGPT tool calling, and summarizes traps like infinite tool loops and blind trust in tool output, plus five convergence practices.
ChatGPT Is Not an AI Agent: Understanding the Core Difference in One Diagram
ChatGPT answers; AI Agents act. Using a flight-booking example, this article breaks down the Agent's think-execute-observe loop and how LLM, tools, and memory form an autonomous AI system.
How to Truly Test AI Agents Before Production? A Practical Testing and Monitoring Framework
BotGage founder Pramin Pradeep shares a three-step framework for testing AI Agents before production: Agent registration visibility, red teaming, and production observability — plus a real case cutting QA costs from $180K to $80K and an analysis of IT roles shifting to Agent supervisors.
How to Choose an AI Agent Development Course: Five Pitfall-Avoidance Guidelines
A mechanical-engineering career changer's two-year pitfall-tested checklist for screening AI Agent development courses: complete delivery pipeline, resume-worthy projects, real development perspective, update frequency, and coaching feedback—with concrete judgment methods and pitfall-avoidance tips.
How Does an AI Agent Control Your Computer? A Full Breakdown of Hermes' Three-Layer Toolchain
Bilibili creator Xiaoyou breaks down Hermes' three-layer toolchain for operating a computer: the built-in Terminal, file modification, and code execution foundation; optional web search, vision analysis, speech synthesis, and browser/desktop control; and an external ecosystem freely extended via CLI and MCP—plus why SSH connects but still can't operate.
How Vercel Agent Safely Accesses Production: A Plan-Driven Permission Model
How does Vercel Agent safely access production? A breakdown of its plan-driven permission model: scopes, OAuth tokens, and runtime checks.
Chatbots, Workflows, and AI Agents: The Three Layers of AI Capability Explained
Everyday examples explain the three layers of AI capability: chatbots, AI workflows, and AI Agents—from passive answering to preset automation to goal-driven autonomous decision-making, with RAG vs. Agent clarified.
AI Agents vs. ChatGPT: The Paradigm Shift from Passive Responses to Autonomous Action
Explaining the essential differences between AI Agents and ChatGPT: from passive chatbots and preset AI workflows to LLM-driven autonomous agents, covering the ReAct framework, self-correction loops, and human-in-the-loop safeguards.
MCP Protocol Explained: How AI Agents Connect to External Tools
What is MCP (Model Context Protocol)? From APIs and Function Calling to MCP's three-layer architecture, this article explains how AI Agents connect to external tools like calendars and email, and reveals two major security risks—prompt injection and tool poisoning—along with four protection principles.
Hive Intelligence: Filling the Real-Time On-Chain Data Gap for AI Agents
Hive Intelligence connects AI Agents to real-time crypto prices and token security checks via MCP servers with one-command deployment. This article reviews its multi-agent architecture, free quota, pricing, and the real value and limits of the Quaza Rewards token incentive model.
MOVO AI: The First Long-Form Video AI Agent on Mobile
MOVO AI is the first mobile long-form video AI Agent: start from shooting, upload, or an idea, auto-plan and generate with character/scene consistency, keep working after lock screen, and review edits via chat. This article analyzes its positioning, Agent value, and key open questions.
What Exactly Is an AI Agent? A Clear Explanation of How It Works
What's the difference between an AI Agent and a chatbot? This article breaks down how AI Agents work: the look-think-act-check loop, tool use, failure rates, and prompt injection risks.
How Do AI Agents Autonomously Complete Complex Tasks? The ReAct Framework Explained
What's the core difference between an AI Agent and a chatbot? Using competitive research as an example, this article breaks down the ReAct framework's three-step loop of reasoning, acting, and observing, explaining how Agents autonomously decompose tasks, call tools, spot data gaps, and dynamically adjust plans to ultimately deliver complete results.
ChatGPT Agent Mode Quick Start: A Multi-Step Task Automation Tutorial
How to use ChatGPT Agent mode? This guide breaks down the enabling steps: open the Tools dropdown from the Composer, select Agent Mode, and clearly describe multi-step goals, plus browser interaction in the VM, data analysis, file generation, and real-time interrupt controls.
How ChatGPT and AI Agents Actually Work: A No-Math Breakdown of the Core Mechanics
A no-math breakdown of how ChatGPT and AI Agents actually work: from tokenization, neural network weights, and attention to word-by-word generation and the Agent tool loop. Includes three practical principles for using AI at work to help you understand why models make mistakes, why long conversations fail, and why Agents need strict authorization.
ChatGPT vs. AI Agent: One Word Apart, Worlds Apart
What's the difference between ChatGPT and an AI Agent? Using a Jaipur travel-planning case, we explain why ChatGPT only gives answers while AI Agents take action, and the three capabilities: task decomposition, tool calling, and the execution loop.
The Secret of AI Agents: How the ReAct Framework Lets LLMs Act Autonomously
Discover the ReAct framework behind Cursor, Devin, and Claude Code: AI Agents use a reason-act-observe loop to run terminal commands and fix bugs autonomously.
AI Coding Assistant Fixed the Bug, Then "Lost Its Memory" in the Next Session
A Reddit developer complains that an AI coding assistant fixed a bug but completely forgot the fix in the next session, revealing the pain of missing session memory in AI Agents, analyzing causes, impacts, industry solutions, and practical advice.
The Essential Difference Between Chatbots and AI Agents: Crossing from Conversation to Execution
What's the difference between a chatbot and an AI agent? Based on the MCP series on the Learn Tech channel, this article uses examples like booking flights and summarizing notes to explain: a chatbot only gives answers, while an agent actually completes tasks through a think-act-observe loop. It breaks down the four core components—model, tools, memory, and goal—as well as MCP's role as a bridge, and discusses agents' applicable boundaries and permission risks.
I Questioned the AI Agent, and It Changed My System Design
A real account of collaborating with the built-in AI Agent Spaceship: by questioning why the index router doesn't mount decision models, the Agent responded and changed the system design. Exploring the collaborative value and usage tips of AI Agents in architectural decisions.