The methodology
How product and engineering build agentic features together. One seed, shared scorers, and a loop that keeps quality honest.
How product and engineering build agentic features together. One seed, shared scorers, and a loop that keeps quality honest.
Define goals, capture user stories, set acceptance criteria.
Know what to build and how it'll be graded.
Build the agent scaffolding, plug in the externally versioned prompts, hook up tools, observability, and evaluations.
See what works in production and what doesn't.
Review and label new production data, draw conclusions, prioritize fixes and new features by impact.
From specs to datasets.
Prompt Build a prompt from your business goals, or check how well an existing prompt serves your purposes.
Dataset No data? No problem. Synthesize a dataset that can be directly use to measure quality.
Scorers Create grading rubrics tailored to what's important for your business.
Understand where you're at. Know where you're headed.
Measure everything you need and nothing you don't.
Quantify your goals and track your progress against them over time.
Compare options, make informed tradeoffs.
Distribute responsibilities, enhance collaboration.
Define what good output looks like through natural language. No Python, no judge harness. Anyone on the team can author rigorous evaluations.
Everyone gets a direct, versioned handle on the prompt. Experiment, tweak, roll back without touching the codebase or breaking trust.
Try a new model and see the impact yourself. Run your evaluations, compare the scores, and ship the swap without engineering tickets.
Let everyone do what they're best at.
The seed and the scorers: what to build and how it's graded. Product defines good, in plain language, without touching code.
The scaffolding, the prompt, the architecture. Engineering makes it work and keeps it observable, without owning the definition of good.