# Build, test, and improve AI in one workflow.

> Compare prompts, models, and agents, catch regressions, and debug failures using the SDK, CLI, IDE, and Git workflow your team already uses.

## Building AI is easy. Improving it is the hard part.

Teams can build an AI prototype in days, but turning it into a reliable production system requires hundreds of decisions across prompts, models, datasets, tools, and architecture. Without a shared development workflow, each change becomes another experiment.

The teams that win are measuring every change, learning from failures, and carrying what works directly into production.

- EU AI Act
- ISO/IEC 42001
- NIST AI RMF
- OSFI E-23


## By the numbers

| Stat | Meaning |
| --- | --- |
| <5min | from an AI change to a measurable pass or fail result |
| One | command to test prompts, models, and agents before they ship |
| 10x | iteration velocity from testing hypotheses instead of changing prompts blindly |


## Between prototype and production, developers are flying blind.

Teams change prompts, swap models, update datasets, and rework agent workflows across notebooks and homegrown tools. Results are difficult to compare, successful configurations are hard to reproduce, and production failures rarely inform the next version.

### No reliable baseline for the next experiment.

Prompt, model, and agent changes are difficult to compare when every experiment uses different datasets, metrics, and configurations.

### Experiments and results scattered across notebooks and docs.

Each team maintains its own evaluation harness, prompt registry, datasets, and results. Successful experiments are difficult to reproduce, share, or maintain.

### Production failures never make it back into development as test cases.

Incidents are patched and forgotten instead of becoming regression tests. The same failure can return because the development workflow never learned from production.


## One development workflow from prototype to production.

Openlayer connects experiments, evaluations, traces, regression tests, and production feedback in one developer workflow. Teams can test every change, understand why it passed or failed, and keep the same standards running after deployment.

Every result stays connected to the version, dataset, and configuration that produced it. Governance evidence builds automatically in the background.


## From prototype to production

### Dataset slicing and cohort analysis

Analyze performance across dataset versions, time periods, user cohorts, and subpopulations to find failures hidden by aggregate scores.

### Custom metrics

Define business-specific metrics in code or natural language, including custom rubrics, deterministic checks, and adversarial tests.

### Experiment tracking and version comparison

Track every prompt, model, dataset, and architecture change. Compare results across versions and return to any previous configuration with complete history.

### Production-to-dev feedback loop

Bring failed production traces directly into development and convert them into regression tests, so every incident improves the next release.


## Build, test, and improve in one workflow.