The article distinguishes between three distinct layers in AI agent architecture: agent harnesses, agent frameworks, and the Model Context Protocol (MCP). Agent harnesses, like those in OpenAI's Codex and Anthropic's Claude Code, are opinionated systems that manage the entire agent execution loop, state, tool usage, and permissions. Agent frameworks, such as LangGraph and the OpenAI Agents SDK, provide libraries of primitives for composing agents, offering components for model clients, tool abstractions, and graph orchestration, but leave policy decisions to the developer. The Model Context Protocol (MCP), governed by the Linux Foundation's Agentic AI Foundation, is a wire protocol that standardizes communication between an agent host and its tools or resources, without managing the execution loop or agent state. AI
IMPACT Clarifies distinctions between agent architecture components, aiding developers in choosing the right tools for agent development.
RANK_REASON The article is an analytical piece comparing and contrasting different layers of AI agent architecture, rather than announcing a new product or research breakthrough.
- A2A
- Agent framework
- Agentic AI Foundation
- AGENTS .md
- Anthropic
- Claude Agent SDK
- Claude Code
- codex
- goose
- Harness.io
- langgraph
- Linux Foundation
- MCP
- Microsoft Agent Framework
- Model Context Protocol
- OpenAI
- OpenAI Agents SDK
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
