#Agent mode
5059 related articles
·2 min
GPT-5.6 Three-Tier Model Breakdown: How Sol/Terra/Luna Rewrite the Agent Cost Structure
OpenAI releases GPT-5.6 with three tiers: flagship Sol, balanced Terra, and budget Luna. Sol leads competitors by 13 points on Agent benchmarks at one-quarter the cost, rewriting Agent economics.
Read more →
·4 min
GPT-5.6 In-Depth Analysis: Model Architecture, Performance Benchmarks, and a Complete Guide to the Agentic Era
A comprehensive analysis of GPT-5.6: the Sol/Terra/Luna product-line positioning, MoE architecture, 1.5M token context, coding agent benchmarks, and the shift from Chatbot to Agent.
Read more →
·7 min
GPT-5.6 and Grok 4.5 Launch Simultaneously, Meta Unveils Agent-Based Image Model
OpenAI GPT-5.6 expands its preview, xAI Grok 4.5 opens access at the same time, Meta releases the Agent-based Muse Image model and Muse Video model, while Apple and DeepSeek launch in-house AI inference chip programs. A quick look at the week's five biggest AI stories.
Read more →
·7 min
Building AI Agents in Rust: The GAIA Benchmark Reveals the Capability Ceiling of Large Models
A Rust-based AI Agent evaluation framework uses the GAIA benchmark to compare GPT, Claude, DeepSeek and other models with no tools. Results show pure LLMs cap at ~25% accuracy, revealing why tool use is decisive for Agents.
Read more →
Local Coding Agents in Practice: A Com…
·6 min
Local Coding Agents in Practice: A Complete Guide to Replacing Your Claude Subscription with Open-Source Models
An in-depth look at local coding agents—core concepts, advantages, and real challenges. Compare against Claude Code and learn to build a zero-subscription, private AI coding workflow with open-weight models.
Read more →
·5 min
From Large Models to AI Agents: A Complete Analysis of the Four Evolutionary Stages and Four Major Commercial Tracks
Prompt engineering and RAG can no longer meet enterprise digital transformation needs—AI Agents are the key. This article breaks down the four evolutionary stages of large model deployment and the four major Agent commercial tracks.
Read more →
·2 min
Deep Dive into Tencent Hunyuan Hy3: How a 295B MoE Model Cracks the Agent Deployment Challenge
Tencent Hunyuan Hy3 launches with a 295B MoE architecture activating just 21B params and 256K context. Hallucination cut from 12.5% to 5.4%, MRCR nearly doubled, with MTP and EAGLE decoding and Day-0 SGLang support.
Read more →
·6 min
GPT-5.6 Now Widely Available: The Era of Three-Tier Models and Multi-Agent Orchestration Arrives
OpenAI releases GPT-5.6 (SOUL/TERRA/LUNA), with Ultra mode running four agents in parallel; Meta launches Muse Spark 1.1 with million-token context; ChatGPT desktop unifies Chat, Work, and Codex.
Read more →
·2 min
GPT-5.6 Opens to All: Three-Tier Model Lineup + ChatGPT Work Agent Reimagines the Workflow
OpenAI launches GPT-5.6 with three tiers — Sol, Terra, Luna. Terra cuts price in half; Luna inputs at $1/M tokens. ChatGPT Work agent automates tasks end-to-end; Codex merges into a unified platform.
Read more →
·6 min
GPT-5.6 Fully Available: A Deep Dive into the Sol/Terra/Luna Three-Tier Models and Four-Agent Parallelism
GPT-5.6 is now officially available to all users, launching the three-tier Sol, Terra, and Luna models with four-agent parallelism. An in-depth look at the official benchmarks, API pricing, safety, and Ultra mode.
Read more →
·7 min
ManagedAgents.sh: A Deep Dive into the Model-Agnostic Open-Source Managed Agent Platform
ManagedAgents.sh is a model-agnostic managed agent platform from OpenComputer, supporting Claude, Pi, and Codex runtimes with Slack and GitHub integration.
Read more →
·6 min
A Beginner's Guide to AI Agents: How Large Models Are Driving a New Computing Revolution
What is an AI Agent? This article systematically explains the core architecture of AI agents (LLM + Planning + Memory + Tools), how they differ from ChatGPT, their combination with robots, and why developers must master Agent development skills.
Read more →
·5 min
A Programmer's Guide to Large Language Models: The Complete Path from Prompts to Agents
A tailored large-model learning path for ordinary programmers: from prompt engineering, API calls, and LangChain, to RAG, Agents, fine-tuning, and enterprise deployment—six steps to build AI application skills fast.
Read more →
·4 min
Building an AI Agent Framework from Scratch: Deep Dive into Tool Calling, Memory, and Dual-Model Scheduling
Build an AI Agent from scratch — no frameworks. Deep dive into Function Call schema design, MCP remote mirroring, dual-model routing, and short-term memory management.
Read more →
·7 min
Claude Sonnet 5 Released: The Most Powerful Agent-Capable Mid-Tier AI Model
Anthropic releases Claude Sonnet 5, its most agentic mid-tier model with planning, browser/terminal tool use, and autonomous execution—bringing flagship Agent capabilities at significantly lower cost.
Read more →
·7 min
Claude Sonnet 5 Released: The Most Capable Agent Model at Mid-Tier Pricing
Anthropic releases Claude Sonnet 5, its most agentic Sonnet model yet. With planning, browser/terminal tool use, and autonomous execution, it brings flagship Agent capabilities to mid-tier pricing.
Read more →
·5 min
Cursor Reshapes AI Programming: Cloud Agents, the Origin Platform, and a Custom-Built Foundation Model
Cursor launches three major products: cloud agents on mobile, Origin — an agent-native Git platform challenging GitHub, and a custom foundation model with 10-20x compute. AI coding enters the Agent-First era.
Read more →
·7 min
AI Agent Development: A Four-Stage Competency Model from 15K to 40K RMB Salary Growth Path
A detailed four-stage competency model for AI Agent development: from Python/RAG basics (15K) to workflow orchestration (20K), inference optimization (30K), and Agent cluster governance (40K RMB).
Read more →
·6 min
Getting Started with LangChain: A Deep Dive into Model and Agent Core Concepts
Deep dive into LangChain's core Model and Agent concepts, covering unified model interfaces, agent tool calling, middleware mechanisms, and key principles for building LLM applications.
Read more →
·3 min
Agentic Coding: How AI Agents Are Reshaping Legacy System Modernization
Developers spend 60%+ of their time understanding legacy code. Agentic Coding uses AI Agents to autonomously explore codebases, map hidden dependencies, and generate tests—turning legacy modernization from risky archaeology into a controlled engineering process.
Read more →