Promptic

Promptic takes a prompt plus a set of labeled examples and searches for a rewrite that scores better on the metric you chose. Each candidate comes with a score, so you compare versions instead of arguing about wording. The tuned prompt stays readable, which matters when a human has to maintain it six months later. Prompt engineering judgment still decides which examples go in, because the search is only as good as the test set.

Point the same test set at two or more models and the platform reports quality, cost, and latency side by side. That answers the question most teams get wrong by default, which is assuming the most expensive model is also the safest. You can keep a cheaper candidate for simple requests and route the hard ones elsewhere.

For agents, Promptic tests different architectures and tool routing rather than a single prompt string. You see which path reaches a correct answer and what it costs in calls. This is the part that separates it from prompt-only tools, since most agent failures come from a wrong tool choice rather than bad wording.

The SDK sends every LLM call, tool call, and workflow step out as OpenTelemetry spans. You get timing and spend per step, and the trace itself becomes raw material for tuning. Tracing is the doorway here, not the product's end goal.

You can import examples, generate more with AI assistance, and build judges that score outputs automatically. Team plans and above add AI-assisted data and judge generation, which shortens the path from "we have 30 examples" to "we have 300." Judging criteria stay editable, so a domain expert can correct a judge that scores the wrong thing.

Everything in the dashboard is reachable through the REST API, the SDK, or the CLI. Business plans add CI/CD gates that fail a build when the eval score drops, plus role-based access control. A coding agent can also run the tuning loop itself through the API, which suits teams that already automate work in their repository.