Foundations
6 minWhat is harness engineering (and why a GPT with tools is not enough)
Dani installed five MCPs and the agent shined in the demo — but broke on the third real task. The problem was not the model: it was the harness.
Read guide →Our school
Learn how AI agents work — from anatomy to step-by-step guides.
Anatomy of an agent
A harness combines loop, MCPs, skills, memory, and verification — click each piece to explore.
Build agents that don't break in production
Harness engineering is the software layer around the LLM: loop, MCPs, memory, and verification. These guides take you from demo to deploy — step by step, with the MCP directory as a supporting tool.
Why harness engineering?
A GPT with tools is not an agent. It is an engine without a car. Harness engineering is everything else: the loop that picks the next step, the tool catalog exposed to the model, memory across turns, and guardrails before a destructive action runs.
Most teams install five MCPs, nail an impressive demo, and break on the third real task. The problem is almost never the model — it is the harness. In guide 1 we explain the pieces and why they matter before you pick servers.
Once you know which pieces you need, the MCP directory lets you search by intent — for example an agent that reads Linear, queries Postgres, and alerts on Slack — and copy configs ready for Claude Code or Cursor.
Learning path
Four guides in sequential order. Each one assumes the previous — start with guide 1 if this is your first time with agents.
Tool design
7 minHow to choose MCP servers for your agent (without installing 40 repos)
A README with 200 MCPs paralyzes you. Here are concrete criteria to filter by intent, maintenance, and scope — before touching package.json.
Read guide →Reference implementations
6 minYour first MCP stack: Claude Code and Cursor step by step
Three MCPs — data, action, notification — and you have a minimum viable agent. Here is the example stack, the stack builder, and how to copy the config.
Read guide →Security & verification
7 minFrom prototype to production: permissions, verification, and a living catalog
The demo worked. In production the agent deleted something, burned tokens, or called a deprecated API. Here are least-privilege permissions, human-in-the-loop, and signs of an abandoned MCP.
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