Enterprise practice guide
How Experience Distillation and MoE Training Apply to Enterprise Work
Enterprise experience is not every conversation and log copied into a dataset. Only formal executions with complete identity, context, tool traces, human corrections, and business outcomes can become traceable evidence.
01
Evidence selection determines the training ceiling
Preview conversations, anonymous logs, incomplete runs, and samples without outcomes should not enter training by default. Evidence must connect workspace, employee version, authorization subject, plan, tools, corrections, and terminal outcome.
02
Training assets need their own governance contract
Moving from evidence to training data requires scope authorization, sensitive-data handling, deduplication, quality gates, contamination checks, data splits, and an evaluation contract.
- Lineage from evidence to derived sample
- Purpose, retention, and deletion ownership
- Train/validation/test isolation
- Baseline, candidate, and regression suite
03
MoE is a model provider, not a governance bypass
A trained local MoE joins the digital employee runtime through common model routing, health checks, latency and cost measurement, baseline evaluation, canary, and fallback. Identity, knowledge access, tool authority, approval, and audit remain in force.