# TechFabric Experiments

> A Databricks adoption accelerator for quality engineering, experimentation, and governed delivery.

Role: Evaluation and quality
Databricks surfaces: MLflow, Unity Catalog, Delta, Lakeflow, Model Serving, AI Gateway
Status: Live across a typed API, a CLI, a studio UI, Cloudflare edge workers for assignment and ingestion, and Temporal for durable workflows.
Documentation: https://experiments.techfabric.com
Canonical: https://www.techfabric.com/accelerators/experiments

---

## The problem

Teams ship a model or an agent and cannot say whether the new version is better than the old one. Evaluation lives in notebooks, experiment assignment is ad hoc, and nothing blocks a regression from reaching production.

## How it works

Built on Databricks SQL, Delta, Lakeflow, Unity Catalog, managed MLflow, Model Serving, AI Gateway, Apps, and Lakebase. Databricks stays authoritative for compute, data, lineage, and governance; Experiments adds A/B assignment, cross-workload evidence, and production quality gates. Native MLflow runs link into the quality centre while their traces and artifacts stay in Databricks.

## What it changes for you

You can answer whether the new version is better, mechanically. Assignment, evaluation, and quality gates run against your own Databricks data, so shipping a model or agent change stops depending on somebody's judgement call.

## Questions

### Does this replace MLflow?

No. Databricks stays authoritative for compute, data, lineage and governance. Native MLflow runs are referenced rather than copied, so datasets, traces and artifacts stay where they are, and Experiments adds assignment, gates and cross-workload evidence on top.

### How do we know a new version is actually better?

Assignment, evaluation and quality gates run against your own Databricks data, so the comparison is mechanical. A failed evaluation blocks the promotion. When the thing being scored is a Genie space that executives have already stopped trusting, the named engagement is Genie Accuracy at /databricks/genie-accuracy.

### Can we run experiments on agents as well as models?

Yes. The same gates apply to an agent version as to a model version, which is what lets you ship agent changes at a pace without guessing at the effect.