AI agents are making consequential decisions autonomously. When something goes wrong, you need to know exactly what happened — and prove it. That is the problem NovaFabric is built to solve.
Reproducibility is a solved problem in traditional software. You commit code, pin dependencies, run the same inputs, get the same outputs. With AI agents, none of that holds. Model weights change, API behavior drifts, tool calls are non-deterministic. A run that worked on Tuesday may fail on Wednesday with no explanation.
This matters more as agents take on more consequential work: running scientific benchmarks, making financial decisions, operating in regulated environments. At that point, 'it worked on my machine' is not an acceptable answer.
NovaFabric introduces the run capsule as the atomic unit of evidence. A capsule is a self-contained directory that records everything about a single agent run: every model call, every tool invocation, every environment variable, every input and output. It is portable, open, and human-readable.
The goal is not to make AI agents deterministic — that is not possible. The goal is to make their behavior inspectable, comparable, and verifiable after the fact. Evidence infrastructure for a world where agents act autonomously.