Harness Engineering Anti-Patterns AP10. The Unobservable Black Box 🎯 Point Was it the model, the prompt, the tool, the context, or the environment? Nobody can tell. A harness where improvement runs on superstition and intuition is a harness that cannot improve. ❗ Problem Failures can't be attributed to subsystems, so no one knows what to fix. Improvement becomes superstition and guesswork, and the harness's retrospective loop stops turning. Combined with "metric monoculture" — tracking only a single metric — unmeasured qualities silently degrade. 🔍 Mechanism & Symptoms Observability is unglamorous infrastructure work, and agents "mostly work," so this anti-pattern gets deprioritized. But without the ability to attribute failures to subsystems, the retrospective loop can't run and the harness becomes unimprovable. Further, tracking a single metric (e.g., success rate only) creates "metric monoculture" where unmeasured virtues (review time, regression rate, code maintainability) quietly suffer. Symptoms: "why did it fail? no idea" is frequent, improvement efforts default to "tweak the prompt," model vs. harness issues are indistinguishable, and success rate improves while reviewer frustration grows. 📋 Scenarios - An agent fails a task but no one can determine whether it was model reasoning error, insufficient context, a tool bug, or an environment issue. The team repeats "let's make the prompt more detailed" as symptomatic treatment. - Success rate is tracked as the sole metric. It improves to 80%, but no one notices that review time for successful cases has tripled. - A model upgrade shows no performance change. Whether it's a model issue or harness scaffolding constraining performance (AP3) can't be distinguished, and investment decisions become superstition. 🛡 How to Avoid - Trace decisions, tool calls, and context transitions, making them attributable to the 7 subsystems (perception, action, feedback, control, memory, guardrails, interface) - Measure in bundles, not single metrics (success rate, intervention rate, rework rate, regression rate, cost, review time, confidence calibration) - A/B test harness changes with the model held fixed to attribute improvements to the harness - Invest in harness observability as "unglamorous but essential infrastructure" and build it as the foundation for improvement loops #HarnessEngineering# #AIAgent#