#multi-agent decision framework
778 related articles
Deep Dives·3 min
Single-Agent vs Multi-Agent: A Three-Step Decision Framework for Choosing the Right Architecture
Based on Anthropic's engineering practices, a detailed three-step decision framework for single-agent vs multi-agent architecture: bottleneck identification, technical feasibility, and business value filtering.
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·2 min
Cogpit: A Self-Hosted Web Monitoring Console for Remote AI Coding Agents
Cogpit is an open-source self-hosted Web UI for remote Claude Code and Codex AI coding agents. Monitor in real time, manage multiple machines, and respond to permissions without SSH.
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·5 min
Tandem: A New Species Reinventing Office Leasing Brokerage with AI
Tandem is an AI-native office leasing brokerage using agentic AI to provide brokers with real-time listings, deal analytics, and landlord flexibility insights, transforming market intelligence from personal experience into system capability.
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·3 min
Building a GitHub Code Review Bot from Scratch: A Hands-On Guide to AI Agent Automation
A detailed guide on building a GitHub code review bot from scratch, covering cloud deployment, secure sandboxes, Vercel AI SDK, and multi-agent collaboration for automated development workflows.
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·3 min
AI Cross-Validation: A Practical Guide to Using Gemini to Review ChatGPT Outputs
Explore cross-validation methods using Gemini to review ChatGPT outputs. Analyze the value and limitations of AI peer review with a rational multi-model collaboration framework.
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·3 min
Building an AI Agent from Scratch: A Full Development Process Retrospective and Pitfall Guide
A complete guide to building AI Agents from scratch based on real developer experiences: task selection, tool comparison (no-code vs frameworks vs hand-written), stability challenges, and evaluation criteria.
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·3 min
DeerFlow: Deep Dive into ByteDance's Open-Source Long-Horizon SuperAgent Framework
Deep analysis of ByteDance's open-source DeerFlow long-horizon SuperAgent framework, covering six core components, architecture design, use cases, and industry significance.
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·3 min
Getting Started with MARL (Multi-Agent Reinforcement Learning): A Complete Path from Theory to Code
A systematic guide to learning MARL from theory to code, covering CleanRL, PettingZoo, PyMARL tools, IQL/VDN/QMIX/MADDPG algorithm progression, and practical tips for bridging theory and implementation.
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·2 min
AI Agents Running a Real Company: Experiment Results and Capability Boundary Analysis
What happens when AI agents are tasked with running a real company? This analysis examines agent performance, critical shortcomings, and practical enterprise deployment advice.
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·3 min
Graph Engineering: The Core Paradigm and Design Patterns for AI Agent Orchestration
Explore how graph engineering uses state machines and directed graph structures to constrain AI agent behavior, covering reflection, routing, human-in-the-loop, and parallel execution patterns.
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·3 min
Graph Engineering: The Core Paradigm and Design Patterns for AI Agent Orchestration
Deep dive into how graph engineering uses state machines and directed graphs to constrain AI agent behavior, covering reflection, routing, human-in-the-loop, and parallel execution patterns.
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·3 min
What Kind of GUI Do AI Agents Need? The Evolution from Chat Boxes to Task Consoles
Exploring GUI design for AI Agents: why chat boxes fall short, and how ideal agent interfaces need task visualization, human-in-the-loop intervention, state presentation, and multi-agent orchestration.
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·3 min
Claude Opus 5 Plays Pokémon: A 12-Hour AI Experiment in Multi-Agent Loop Architecture
Deep analysis of Claude Opus 5 playing Pokémon for 12 hours via multi-agent loop architecture, exploring Agent design patterns, long-horizon planning, and AI Agent trends.
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·3 min
Claude Opus 5 Plays Pokémon: Analyzing a 12-Hour AI Experiment Under Multi-Agent Loop Architecture
In-depth analysis of Claude Opus 5's 12-hour Pokémon gameplay through multi-agent loop architecture, exploring multi-Agent design, long-horizon planning, and AI Agent trends.
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·3 min
AI Agent Authentication and Authorization: Core Analysis of the IETF Draft and Security Best Practices
Deep analysis of the IETF Internet-Draft on AI Agent authentication and authorization, covering identity attribution, delegation chains, least privilege, OAuth 2.0 extensions, and MCP integration.
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·2 min
LangChain or LangGraph? Framework Choices for Production-Grade AI Applications
In-depth analysis of LangChain vs LangGraph differences, why teams are migrating to LangGraph for production AI apps, and framework selection guidance based on project complexity.
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·2 min
LangChain or LangGraph? Choosing the Right Framework for Production-Grade AI Applications
In-depth analysis of core differences between LangChain and LangGraph, exploring why more teams are migrating to LangGraph for production AI apps, with framework selection guidance.
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·3 min
Greplica: A Self-Updating Code Knowledge Base Built for AI Coding Agents
Greplica is an open-source self-updating Wiki for coding agents that automatically extracts decisions, constraints, and gotchas from sessions, enabling shared codebase memory across agents and developers.
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·3 min
Greplica: A Self-Updating Code Knowledge Base Built for AI Coding Agents
Greplica is an open-source self-updating wiki for coding agents that auto-extracts decisions, constraints, and gotchas from sessions, enabling shared codebase memory across agents and developers.
Read more →
·2 min
Task Monki: An Open-Source Multi-Agent Collaborative Programming Workflow Platform
Task Monki is an open-source desktop app that lets coding agents handle the full dev workflow from task planning to Pull Request, with multi-agent parallel execution, code review, and collaborative discussion.
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