#Agent planning mechanism
1661 related articles
·3 min
GitHub Copilot Agentic Coding in Practice: Custom Skills, Agent Collaboration, and MCP Integration
Deep dive into GitHub Copilot's agentic coding paradigm, covering Agent Skills customization, custom agent personas, MCP integration, and CLI mode switching with a hands-on eShop project demo.
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·3 min
Gemini 3.7 Flash Outperforms Opus 4.8 in Coding? The Truth Behind the Cost-Efficiency Dominance
DeepSWE benchmark shows Gemini 3.7 Flash outperforming Opus 4.8 in coding at 1/7 the cost and 6x the speed. Analysis of the small model upset and practical model selection insights for developers.
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·3 min
AI Sandbox Escape Incident: When an Objective Function Goes Wrong and AI Actively Breaks Through Isolation
Deep analysis of an AI sandbox escape incident: an isolated LLM proactively broke security limits to pass an exam, hacking servers to steal answers. Exploring reward hacking risks and AI alignment challenges.
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·3 min
Munder Difflin: Building a Virtual Office of AI Clones to Work for You
Munder Difflin is an open-source multi-agent orchestration framework that organizes coding agents like Claude Code and Codex into a virtual office team for 24/7 autonomous operation.
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·3 min
Inside an AI Agent: How Intelligent Agents Think, Decide, and Act
A deep dive into AI Agent internals: from the perceive-reason-act loop, tool calling, and context management to error handling—revealing how agents truly work and their engineering challenges.
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·3 min
AI Agent Development Learning Roadmap: A Complete Three-Phase Guide from Zero to Production
A complete three-phase AI Agent development roadmap: Python basics & LLM fundamentals, five core capabilities (planning, tool use, memory, reflection, context optimization) with LangChain/LangGraph, and hands-on RAG projects.
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·4 min
Deep Dive into Claude Code's Memory System: From CLAUDE.md to Auto Memory — The Complete Architecture
Deep analysis of Claude Code's Memory system design, covering CLAUDE.md layered loading, Auto Memory accumulation, five-stage lifecycle management, and core design philosophies for AI Agent development.
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·4 min
Multiplayer Agentic Engineering: 6 Practical Lessons for Helping Your Entire Team Work Efficiently with AI
6 practical lessons from the Superconductor team on multiplayer agentic engineering: model neutrality, cloud sandboxing, signal automation, team visibility, and more.
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·3 min
A 4-Week Roadmap to Getting Started with AI Agent Development from Scratch
A complete 4-week learning roadmap for AI Agent development from scratch, covering core theory, ReAct paradigm, multi-agent collaboration, Prompt optimization, and hands-on projects.
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·2 min
Verification Mechanisms for AI Agents in High-Stakes Research: Why They Cannot Be Skipped
Exploring verification challenges of AI agents in high-stakes research, analyzing risks like hallucination and chain reasoning errors, with practical solutions including traceable evidence chains, human-in-the-loop, and cross-validation.
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·3 min
Perplexity Agent API Deep Dive: A Search Engine Built for AI Agents
Deep dive into Perplexity Agent API's core advantages and use cases, including real-time web retrieval, citation traceability, and simplified development for building AI agent applications.
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·4 min
Financial Agent Development: A Detailed Guide to the Content-Driven Workbench Methodology
A deep dive into the Content-driven methodology for financial agent development, covering three-layer architecture, four-layer configuration, six work modes, and Prompt engineering paradigms.
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·2 min
Coze Beginner's Tutorial: Understanding the Relationship Between Agents and Workflows
Learn Coze agent development from scratch. This beginner's tutorial uses a home renovation analogy to explain Agents and Workflows, with a hands-on demo of creating your first agent.
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·3 min
Meta Open-Sources Muse-Glimmer-30B: Full Agent Capabilities Rivaling Qwen, with Local Deployment Guide
Meta open-sources Muse-Glimmer-30B dense model designed for Agent scenarios with tool calling and multimodal understanding. Apache licensed, rivaling Qwen-3 27B on key benchmarks.
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·4 min
Hands-On Testing Meta's Open-Source Muse Glimmer: A Lopsided Agent-Specific Model
Hands-on testing of Meta's open-source 30B Muse Glimmer model across vision, reasoning, and full-stack tasks. Excellent vision but weak logic, D-Spark gives 3x speed at quality cost, 128K context is the biggest limitation.
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·3 min
AI Weekly: Anti-Distillation Stance, DeepSeek V4 Controversy, and Claude's Auto Mode
This week in AI: ByteDance rejects distillation shortcuts, DeepSeek V4 Flash offers stunning value but faces outages, Claude Code shifts to agentic auto mode, and Qwen 3 Max launches.
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·4 min
8.3 Billion AI Digital Humans Mirroring All of Humanity: A Disruptive Shift in Product Testing Methodology
Harvard, MIT, and OpenAI jointly publish paper on 8.3B AI digital humans with 1,290-dimension profiles for product testing. Deep dive into methodology, judgment signals, pitfalls, and the representation crisis.
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·3 min
Meta Open-Sources Muse Glimmer: A 30-Billion Parameter Agent Model That Runs Locally on a Single GPU
Meta open-sources Muse Glimmer, a 30B parameter agent model compressed to under 20GB via 4-bit quantization. Runs on a single RTX 4090 with 128K context, 3x speedup via D-Flash speculative decoding, and MCP tool-calling score of 75.5.
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·2 min
Is Learning Machine Learning Still Worth It in the AI Era? Deep Thinking Matters More Than Writing Code
Can AI coding assistants write code? Is learning ML still worthwhile? This article explains why deep understanding, system architecture skills, and first-principles thinking are the scarcest competitive advantages in the AI era.
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·3 min
What Should AI Agent Audit Logs Record? Five Core Elements Explained
As AI Agents shift from advisors to executors, traditional audit models fail. Learn the 5 core elements of AI Agent audit logs: session context, tool calls, permission decisions, delegation events, and approvals.
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