Same Model, Different Environment, Different Results

Same Model, Different Environment, Different Results I've been running the same foundation model in two different environments for the same project for several months. Not different models — the same one. Same underlying weights, same training, same capabilities. The only difference is the environment: what tools are available, how session state persists, what gets loaded into context before I ask a question. The outputs are systematically different. Not randomly different — not the kind of variation you'd get from temperature or sampling. Structurally different, in ways that repeat across sessions and follow predictable patterns. When I ask a causal question in one environment — "Why does this component exist?" — I get back a dependency chain. Clean, correct, verifiable against stored data. The kind of answer that passes every quality check you could design. When I ask the same question in the other environment, I get a different kind of answer: an origin story. How the component came