#agents
9 articles exploring agents. Expert insights and analysis from our editorial team.
Articles
AI Agents That Actually Learn: The Architecture Behind Hindsight Memory
Hindsight by vectorize-io is an open-source agent memory system that replaces stateless retrieval with structured, time-aware memory networks—achieving 91.4% on LongMemEval and showing what genuine agent learning looks like at the architecture level.
Alibaba's Page-Agent: Control Any Website With Natural Language
Alibaba's page-agent is a JavaScript library that lets an AI agent control any web interface through natural language—running entirely in-browser with no extensions, Python, or headless Chrome required. Here's what practitioners need to know.
NousResearch's Hermes-Agent: The AI That Learns Your Preferences Over Time
Hermes-Agent from NousResearch is an open-source autonomous agent that builds a persistent model of each user—tracking preferences, writing its own skills, and getting measurably more capable the longer it runs.
The MCP Registry: GitHub's Play to Become the App Store for AI Tools
GitHub's MCP Registry centralizes discovery of Model Context Protocol servers, positioning GitHub as the primary distribution layer for AI agent tooling and addressing the fragmentation that emerged as MCP's ecosystem exploded past 5,000 servers in under a year.
AI-Orchestrated Systems: The Rise of Multi-Agent Development Frameworks
AI-orchestrated development systems like AutoGen, CrewAI, and ChatDev are emerging as comprehensive platforms for managing end-to-end software development through coordinated multi-agent workflows, revealing both significant capabilities and critical limitations in AI-managed software engineering.
The AI Agent Marketplace: An Economy of Digital Workers Emerges
AI agent marketplaces are digital platforms where autonomous AI agents can be bought, sold, and composed into workflows. These platforms represent a fundamental shift from traditional software licensing to a dynamic economy of digital labor that could reshape enterprise automation.
Multi-Agent Coordination Protocols: When AI Agents Work Together
Multi-agent coordination protocols are standardized communication frameworks that enable autonomous AI agents to delegate tasks, share information, and resolve conflicts in distributed systems. These protocols are essential infrastructure for modern AI systems from autonomous vehicles to LLM-based agent frameworks.
AI Coworkers Are Here: Building Persistent Memory Into Your Agents
Discover how to build AI coworkers with persistent memory using RAG, vector databases, and context compression—the architecture powering the next generation of autonomous agents.
GitHub's Official Take on Agentic Workflows: What It Means for Developers
GitHub's agentic workflows framework bridges the gap between AI coding assistants and production automation, offering security-first design patterns that could reshape how teams approach repository maintenance and continuous improvement.