I’m working on a deterministic execution layer for AI systems.
The goal is to treat LLMs as constrained components inside explicitly defined workflows: strict input/output schemas, validated DAGs, replayable execution, and observable failure modes. Most instability I’ve seen in production AI systems isn’t model-related — it comes from ambiguous structure around the model.
We’re exploring this through a project called FACET, focused on making AI behavior testable and debuggable rather than probabilistic and opaque.
Early days, but the direction is clear: less magic, more contracts.
The goal is to treat LLMs as constrained components inside explicitly defined workflows: strict input/output schemas, validated DAGs, replayable execution, and observable failure modes. Most instability I’ve seen in production AI systems isn’t model-related — it comes from ambiguous structure around the model.
We’re exploring this through a project called FACET, focused on making AI behavior testable and debuggable rather than probabilistic and opaque.
Early days, but the direction is clear: less magic, more contracts.