Harness Engineering 101: Prompt Engineering wasn't enough. Neither was context. The harness was.

Short summary

Prompt engineering and context engineering each hit a wall: every session started cold, requiring manual reassembly of your knowledge. Harness engineering solves this with standing infrastructure—CLAUDE.md personalization, indexed memory files, MCP connectors for real access, delegation patterns—so the model starts informed. Agent = Model + Harness; the model is swappable, but the harness is what makes real work possible.

• •Prompt and context engineering each had a ceiling: every morning meant rebuilding context from scratch, making you the bottleneck.
• •Harness engineering = standing infrastructure (CLAUDE.md + memory index + MCP + delegation + skills) so the model starts informed and productive.
• •Five components: personalization, context, action, memory, delegation—with skills and scheduled jobs as force multipliers on top.

Generated with AI, which can make mistakes.