Agent & AI Insights
Claude Code know-how, heterogeneous agents, harness engineering, and more — trends and adoption know-how from the Marblo team
Claude Code + MCP in Real Workflows — Notes from a Korean AI Studio
How a Seoul-based AI agency runs Claude Code with MCP servers across every project. The patterns that actually scale, the integrations that paid off, and the workflow tax we eliminated.
Building Your First Multi-Agent System with Marblo — Hands-On Tutorial
Walk through building a real multi-agent workflow in Marblo, from blank board to production deploy in under thirty minutes. Researcher, writer, fact-checker — heterogeneous models, MCP tools, and observability included.
MCP Servers in Production — Authentication, Rate Limits, and Observability
Building MCP (Model Context Protocol) servers for a hobby project is easy. Running them in production with real authentication, real rate limits, and traces you can debug at 2 AM is a different problem. This is what we learned.
Heterogeneous Agents in Production — Why Single-Model Setups Fail at Scale
After running heterogeneous AI agents in production for 18 months, we measured what single-vendor setups give up. The cost premium, the failure modes, and the team-level patterns that only work when you mix models on purpose.
AI Agent Orchestration Platforms in 2026 — LangGraph, CrewAI, AutoGen, and Marblo Compared
An engineering-grade comparison of the major AI agent orchestration platforms in 2026. Where each one shines, where each one breaks, and which choice fits which workload — from prototype to multi-team production.
Codex 5.5 GOAL Mode — The New Standard for Autonomous Agents
OpenAI Codex's latest GOAL mode is reshaping the autonomous agent paradigm. We analyze the shift from imperative commands to goal-driven execution, and how it compares to Marblo's natural language orchestrator.
Why Heterogeneous AI Agents Beat Single-Model — Claude, GPT, and Gemini on One Board
Why leading AI teams in 2026 are choosing heterogeneous agent orchestration over single-vendor solutions. The performance gap and cost efficiency that comes from role-based model assignment — Claude reasoning, GPT generation, Gemini verification.
Model Context Protocol (MCP) Explained — The Standard for Tool-Wielding Agents
MCP gives AI agents standardized access to filesystems, databases, APIs, and Git. We unpack why MCP became the 'USB-C' of the AI agent industry — and how to integrate it with internal company systems.
Claude Code Subagents vs. Real Multi-Agent Orchestration — What's the Difference?
We dissect the gap between Claude Code's 'subagent' pattern and genuine heterogeneous multi-agent orchestration. Single-model N agents vs. heterogeneous N agents, CLI vs. kanban board, shared context vs. physical isolation.
5 Principles for In-house AI Agent Governance — Design the Trust Hierarchy First
When you adopt AI agents internally, the first question is 'Who is responsible for code an agent wrote?' This article lays out 5 governance principles: privilege separation, audit logs, rollback paths, measurable KPIs, and team training.
Building AI Agents for the Korean Market: Platforms, Regulations, and Real-World Use Cases
How to build and deploy AI agents optimized for Korean business environments. Covers KakaoTalk integration, Korean NLP challenges, compliance requirements, and successful implementation strategies for international companies entering Korea.
AI-Powered Customer Service: Complete ROI Analysis for 2025
A detailed ROI analysis comparing AI-powered customer service with traditional support teams. Includes cost breakdowns, satisfaction metrics, implementation roadmap, and real-world case studies.
AI Agents for Business Automation: Complete Guide 2025
Learn how to automate customer service, sales, and marketing with AI agents using OpenAI's Agent Builder, Response API, and custom implementations.