#AI Agent
1101 related articles
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.
How Does Claude Call Tools It Has Never Seen? MCP and AI Agent Principles Explained
How does Claude call tools it was never trained on? This article breaks down the AI Agent tool-calling loop, Anthropic's three-layer MCP architecture, context bloat and context rot, and production-grade Agent safety design principles including timeout retries, prompt injection, and idempotency keys.
Claude Duet Explained: How Proactive Agents Differ from Claude Code Routines
Claude Duet is Anthropic's proactive agent in research preview, working in the background and generating daily briefings. This article explains how it differs from Claude Code Routines.
AI Agent Draws an Architectural Elevation in 15 Seconds — Open-Source Drawing Software Rewritten by AI
An AI Agent controls open-source drawing tool VectorCraft via MCP Server, executing 1,077 commands with zero errors to draw an architectural elevation in 15 seconds and export editable SVG/PDF.
MCP Security Primer: OWASP Top 10 Risks and the Lethal Trifecta Defense
A systematic breakdown of MCP security risks: from the Postmark backdoor to the OWASP MCP Top 10, how the Lethal Trifecta reveals AI Agent fragility, plus shadow servers and missing audits.
Claude Duet Unveiled: Anthropic Launches a Proactive Personal Assistant
Anthropic launches Claude Duet, a proactive personal assistant seen as a response to Muses, Bots, and Dots. This analysis covers its positioning, competition, technical barriers, and market impact.
Behind the QQ Agent Hype: Ecosystem Chaos and Pitfall-Avoidance Guide as Newcomers Flood In
After QQ Agent's explosive growth, community numbers surged. Bilibili creator Bai Lu complains about common newcomer issues: API configuration errors, model selection confusion, account conflicts, incomplete error screenshots. This article sorts out the technical essence of these high-frequency problems and offers Agent tool pitfall-avoidance advice and a must-read guide for newcomers.
The Most Comprehensive AI Agent Tutorial on Bilibili: From Zero to Practice in Seven Days
Comprehensive analysis of Bilibili's AI Agent tutorial: from ReAct framework, planning & reasoning, memory mechanisms in the cognition part to LangChain & AutoGen practice in advanced part, and enterprise project implementation. Is the seven-day learning path reliable? Who is it for? This article breaks down the course structure and design philosophy.