Every week, the same kind of friction shows up in your agent sessions. A misunderstanding that keeps coming back. A workaround you’ve explained before. That familiar moment of: “Right, I need to tell it this again.”

In this hands-on workshop, you’ll work with an existing agent harness and build a small continuous-improvement loop around it. The routine looks across recent sessions, finds recurring patterns, and helps turn them into concrete improvements: a sharper instruction, a new rule, a skill, a guardrail, or whatever the situation calls for.

You’ll also document the change as an HDR: Harness Decision Record. What pattern triggered it? What evidence did you see? Why is this the right fix? And how will you validate whether it actually worked?

By the end, you’ll have a working improvement routine, your first HDR, and a harness that learns from repeated friction instead of leaving you to catch the same problems manually every time.