# Build AI quality into every release.

> Test every change, catch regressions before release, trace failures in production, and improve the next version without stitching together more tools.

## By the numbers

| Stat | Meaning |
| --- | --- |
| 87% | of developers are concerned about the accuracy of AI agents (Source: 2025 Stack Overflow Developer Survey) |
| 47% | of organizations using AI have experienced negative consequences (Source: McKinsey’s 2025 State of AI Global Survey) |
| 74% | of production AI agents still rely primarily on human evaluation (Source: Pan et al., Measuring Agents in Production, 2025) |


## One engineering workflow across development and production.

### Connect to the stack you already use

Send data through Openlayer SDKs, framework integrations, the Gateway, CLI, or REST API.

Add Openlayer to your existing development and production workflows without rebuilding them.

### Test every change before release

Evaluate changes to models, prompts, data, and agent behavior against defined thresholds.

Compare versions and use test results as a release gate before production.

### Trace every step in production

See prompts, retrieval, model calls, tool calls, outputs, latency, token usage, and cost across a session.

Find where a failure started without piecing together separate logs.

### Improve with production feedback

Keep tests running against live traffic, receive alerts when quality drops, and turn failed production sessions into regression tests for the next release.


## Quality you can ship.

### 6x

increase in deployment frequency

### +53%

increase in engineering throughput

### 100%

regressions caught before production

### 67%

reduction in time to resolve AI failures


## Proof

> Without Openlayer, we'd be blind to how our LLM outputs behave at scale. It's saved us months of engineering time and given us a repeatable way to keep phishing simulations reliable and realistic.
> 
> Daniel Chyan, CTO, Jericho Security


## Ship with confidence