AI Harness Development

Most teams shipping LLM features today change a prompt, eyeball a few outputs, and deploy. When quality drops, they find out from users. An AI harness replaces that guesswork with the discipline software teams already apply to code: every change is tested against known cases before it ships.

We build the evaluation and testing scaffolding around your LLM and agent systems:

• Golden datasets — curated, versioned sets of real inputs with agreed-correct outputs, covering your happy paths and your known failure modes
• Eval suites in CI — automated scoring on every prompt, model, or pipeline change, with regression gates that block deploys when quality drops
• Prompt and model versioning — every change tracked, diffable, and revertible
• Guardrails — input and output validation, safety filters, and fallback behavior for the cases your model gets wrong
• Cost and latency benchmarking — so a “better” model that triples your bill or your response time is a visible trade-off, not a surprise
• Tracing and observability — full visibility into what the model saw, what it did, and what it cost, on every production call
• Human-review loops — structured workflows for the judgment calls automation can’t make, feeding back into your golden datasets

The deliverable

A harness your own team runs on every change — not a report, and not a dependency on us. On the Databricks stack this maps directly to MLflow evaluation and tracing, Unity Catalog governance, and Databricks Asset Bundles for the CI/CD wiring; we build equivalent setups on other stacks.

Engagement shape

Typically four to eight weeks. We start from your highest-risk AI feature, stand up the golden dataset and eval suite around it, wire the regression gate into your existing CI, and train your team to extend the harness to every other AI surface you ship.