Agent & AI Insights
Claude Code know-how, heterogeneous agents, harness engineering, and more — trends and adoption know-how from the Marblo team
- AI Agents
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.
- AI Agents
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.
- AI Agents
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 Agents
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.