Harness design is now part of the craft

A harness is everything around the model: prompts, tools, orchestration, context management, hooks. If you want the from-scratch primer on what a harness is and why it matters, chapter 19 — "What Is an AI Harness?" covers it. This section is the moving-target view. Anthropic published a detailed breakdown of how a harness affects long-running agent performance — two findings stood out.

First: agents lose coherence as context fills. Some models exhibit "context anxiety," wrapping up prematurely. The fix isn't compaction (summarizing in place) — it's context resets: clear the window, start a fresh agent with a structured handoff. Compaction preserves continuity but doesn't give the agent a clean slate.

Second: agents reliably praise their own work when asked to evaluate it. The fix is architectural — separate the generator from the evaluator. A standalone evaluator tuned to be skeptical is far more tractable than making a generator self-critical.

The architecture that emerged: planner → generator → evaluator with Playwright clicking through the running app. Every component encodes an assumption about what the model can't do alone — those assumptions go stale as models improve. Strip non-load-bearing scaffolding when a new model lands. The full article has cost and duration breakdowns.