Observability

Commit Intent in AI Harness Engineering Commit intent is the discipline of having an agent explicitly declare what it is about to do, and why, immediately before it actually invokes a tool — separating the decision from the execution as two distinct steps in the harness. Concretely, before a tool call goes out, the agent emits a short, structured statement: the action being taken, the target, the expected outcome, and often the reasoning that justifies it. Only after that intent is committed does the harness fire the actual tool call. This sounds redundant — the tool call itself already encodes “what” — but it solves several real problems in agentic systems. ...

Model Drift Model drift is the general phenomenon where a deployed model’s predictive performance degrades over time, even though nothing about the model itself has changed. The model is the same; the world it operates in isn’t. Taxonomy Drift is usually classified by what’s shifting in the underlying probability distributions. Data drift (covariate shift) The distribution of input features P(X) changes, but the relationship P(Y|X) stays the same. A fraud detection model starts seeing a higher fraction of mobile-wallet payments — inputs look different, but the rules for “is this fraud” haven’t changed. ...