Most agentic workflows don’t fail because the model isn’t capable enough, but because the environment around it is incomplete. Important context lives in someone’s head, instructions are scattered, and the agent keeps making the same mistakes.

In this full-day workshop, you’ll build an agent harness around a project we provide. You’ll work with instructions, skills, agents, hooks, project knowledge, validation, and persistent context, and learn how these pieces work together to make an agent more reliable.

Then we’ll go one step further: making the harness you built in the morning continuously improve over time. You’ll use it in practice, spot recurring friction, decide what is worth changing, and evolve the harness based on what you learn. You’ll capture those decisions in an HDR and put the updated harness back to work to see whether it actually performs better.

By the end of the day, you’ll have built an agent harness, created a continuous-improvement loop around it, and documented your first improvement as an HDR: Harness Decision Record. Not just a better harness, but one that can keep getting better.