#AI Agent
1060 related articles
IFTTT.ai Launches Major Upgrade: AI-Powered Automation Connecting 1,000+ Services
IFTTT.ai upgrades with AI-driven automation: all plans include core features and 1000+ services, credit-based pricing, unlimited free Applets, AI Agent web monitoring, AI Action assist, and IFTTT Chat for natural language automation.
JIMMY: Control Your PC via WhatsApp Self-Chat, a New AI Agent Concept
JIMMY is an AI Agent on Windows PC that receives commands via WhatsApp self-chat, executing file sending, screenshots, scheduled tasks, with confirm-before-send, /stop interrupt, and undo log. This article analyzes its interaction design and security mechanisms.
Cursor Agent SDK Released: Build Your Own AI Agents with Code
Cursor opens its Agent SDK: Agent-as-Code with Markdown instructions and TypeScript tools, one-command deploy, or self-host as a Node process. Pricing and independent testing still missing.
OpenBot: The Open-Source Project Giving Every AI Colleague Their Own Computer
OpenBot is an open-source CopilotKit project giving each AI Agent its own browser, files, and tools, with actions decided before execution and recorded after, supporting any AG-UI agent. 7,749 stars.
WorkBuddy Complete Guide: How AI Agents Automate File Conversion for You
WorkBuddy hands-on guide: from Doubao giving only plans to AI agents that actually execute—batch-convert PDFs and archive them in three minutes. Includes installation, points system, Codex comparison, and getting-started tips.
How Do AI Agents and Workflows Differ? Understanding the Core Differences in One Chart
What's the difference between AI Agents and workflows? This article breaks down the differences across determinism, information types, failure modes, and cost, with advice on enterprise division of labor: give clear rules to workflows, give understanding and judgment to AI, and keep human confirmation at key nodes.
From 3 Days to Minutes: Apollo Rebuilds Portfolio Construction with AI Agents
Apollo's Reading Ridge and Rowspace used AI agents to cut CLO portfolio construction from days to minutes. How AI crosses the trust threshold in core finance.
5 MCP Servers That Unlock New Skills for Your AI Agents
Five practical MCP servers reviewed: Context 7 for up-to-date library docs, Playwright MCP for real browser control, AgentWire for converting 28 platforms into Markdown, Stripe's official MCP for payments, and CallForMe for phone calls.
DeepSeek Harness Penetration Mode in Practice: Automated SRC Vulnerability Discovery with AI Agents
A detailed guide to installing the DeepSeek Harness desktop client and configuring its penetration mode plugin, demonstrating an AI Agent automating port scanning, web enumeration, and vulnerability validation, plus practical notes on permission control and multi-Agent collaboration.
The Ultimate WorkBuddy Tutorial: AI Agents from Beginner to Real-World Practice
A nanny-level WorkBuddy tutorial for beginners: from core AI Agent concepts to installation and use, comparing onboarding difficulty, office compatibility, and cost with tools like Codex to help you own a digital employee at low cost.
Building Enterprise-Grade AI Agents from Scratch: A Three-Stage Learning Path from Bilibili's Comprehensive Tutorial
Analysis of Bilibili's 748-episode AI Agent beginner course: cognition section clarifies Agent vs. LLM differences, advanced covers LangChain, AutoGen, and multi-Agent collaboration, hands-on implements intelligent customer service and automated data analysis projects, with source code, courseware, and a learning roadmap.
Hermes Agent Self-Evolving Framework: A New Open-Source AI Agent Option
Based on a Bilibili creator's demo, this article analyzes the open-source AI Agent framework Hermes Agent's self-evolution mechanism, its differences from Open Cloud in Token consumption and long-term memory, and the real-world results of automatic skill generation.
Five Common AI Agent Proxy Mistakes: Making Your Agent Blend In
AI agent getting blocked? This article distills five proxy configuration mistakes: country-language mismatch, mid-flow IP rotation, datacenter IPs being detected, shared egress across tasks, and never checking the actual egress—plus fixes like sticky sessions, per-task routing, and logging route.reason.
Dedica: Can Your Website Pass AI Agents' 125 Tests?
Dedica is a new Product Hunt tool that uses 125 free checks to test whether websites work for AI Agents, with a fix for every issue that coding agents can act on.
The Essence of AI Agents: One Loop, Five Core Steps
What is the essence of an AI Agent? This article explains its core loop: send the conversation to the model, check for tool requests, execute tools, and update the conversation until an answer is reached. Learn the three components and max turn limits.
Six AI Agent Frameworks Compared: Choosing Wrong Costs Weeks—How to Choose Right in One Minute
A comparison of LangGraph, CrewAI, OpenAI Agents SDK, Claude Agent SDK, Google ADK, and Microsoft Agent Framework, with a one-minute selection guide.
AI Daily: GPT-6.1 Terra and Kimi K3.1 Pro Lead the Charge as Agent Security Alarms Sound
AI news in 24 hours: OpenAI releases GPT-6.1 Terra that can operate computers, Kimi K3.1 Pro tops open-source leaderboards with weights promised, MiniMax M3.1 Flash weights on Hugging Face, Cursor launches Agent SDK, while Anthropic and Qwen models face agent privilege-escalation security incidents.
AI Agents and MCP Internals: How LLMs Call Tools
A deep dive into how AI Agents and MCP work: how LLMs break free from text-only limits via tool calling, how the agent loop operates, how MCP connects tools and data with a unified standard, plus security risks and boundaries.
Why AI Agent-Recommended Leads Are Worth Following Up: The Four-Element Verification Method
How to verify AI Agent-recommended sales leads? This article introduces the four-element verification method: ICP match fields, dated buying signals, data source traceability, and CRM deduplication—intercepting unqualified leads through sampling audits before sales follow-up, making every recommendation traceable and reviewable.
Long-Running AI Agents Require Multi-Layer Feedback Loops
The LangChain Deep Agents course proposes a loop engineering framework that breaks AI agent feedback into execution, verification, and cross-run improvement loops to build reliable long-running agents.