Every team had an AI stack. Nobody had an AI release contract.
Prompts lived in application code, tools had inconsistent schemas, and quality was judged from a handful of chat transcripts. A provider or prompt change could improve one workflow and silently break another.
There was no repeatable corpus for task success, refusal, safety and groundedness.
Tool descriptions, permissions and side effects varied by framework.
Accuracy, latency and token usage were measured in separate systems.
A release candidate became an evidence bundle.
Prompt, model policy and tool allow-list
Typed tools with scoped identity and risk hints
Happy path, adversarial and edge cases
Model, retrieval and tool spans
Quality, safety, latency and cost budgets
The harness standardized evidence, not creativity.
LangGraph, provider SDKs and custom orchestrators emitted the same run and evaluation contract.
A writing assistant and an account-action agent did not share the same approval burden.
Quality floors came first; the least costly passing route served production traffic.
AI changes became inspectable and reversible.
| Measure | Before | After |
|---|---|---|
| Release evidence | Up to 14 days | Under 2 hours |
| Evaluation coverage | Ad hoc examples | 73% critical paths |
| Token cost per task | Baseline | 41% lower |
| Tool governance | Per application | Versioned MCP contracts |
Turn your AI prototypes into an owned platform.
We can design the minimum evaluation, tracing and tool-governance layer your risk profile needs.