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