#AI Agent
1068 related articles
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.
One Diary Gives AI Persistent Memory: A Semantic Retrieval Solution Tested
A Bilibili creator open-sources an AI persistent memory solution: a lean persona stays in context, while dynamic info is written to a diary and retrieved semantically on demand. Hybrid vector and keyword retrieval, tested with BGE model pitfalls and fixes. Install on GitHub with one command.
Former Cursor Engineer: Is Agent Harness Dead? The Data Flywheel and Three Things the Model Can't Absorb
Former Cursor Agent Harness engineer Jediah Katz breaks down Harness evolution: how the data flywheel lets models absorb coding skills, why Harness isn't dead, and why infrastructure, trust, and efficiency can't be absorbed by models.
Anthropic Donates Agent Skills Standard to AAIF
Anthropic donates the Agent Skills standard to the Linux Foundation's Agentic AI Foundation, the second AI Agent standard to enter neutral governance after MCP.
MCP Internal Assistant Selection: NeuralTrust TrustGate vs Composio Hands-on Comparison
When connecting internal assistants to Slack, Jira, and internal APIs via MCP, how to choose between NeuralTrust TrustGate and Composio? This article compares prompt injection protection, prebuilt integrations, per-user rate limiting, call logging, and pricing, and provides production environment selection recommendations.
Production AI Agent Operations: A Three-Step Framework for Observing, Evaluating, and Optimizing
Azure Cosmos DB team proposes a three-step framework for production AI Agent operations: observe trace tracking, evaluate with LLM as a judge and regression testing, optimize cost across RU, retrieval, memory, and LLM layers.
AI Agent Agencies: One-Time Development Fees or Reselling Platforms?
Should AI Agent agencies charge one-time development fees or resell white-label platforms monthly? A Reddit developer discussion reveals the real challenges of agent deployment: bots get lost, and human fallback is essential. This article analyzes the profit structures and hybrid strategies of both models.