Shared agentsIncidents
Reduce MTTR by connecting alerts, changes, and human insight with AI.
- AI Quality & Reliability
//AI Evals
Ship AI agentsyou can trust.
The quality gate for AI agents, built into your Agent Development Lifecycle. Score every response and gate every release, inside the CI/CD pipeline.
Blind spots
Silent failures show up as a failed gate
An agent can return a confident, plausible, wrong answer with no alert. Every response is scored for faithfulness, relevance, and task completion.
Non-deterministic
Grade quality on a spectrum
You can't write assertEqual for a summarization agent. LLM-as-judge and semantic scoring compare variants side by side.
Gated
Quality becomes a build status
A native pipeline gate blocks the deploy when scores fall below threshold.
//Overview
What are AI evals?
AI evals score an AI agent's outputs against a defined quality bar, such as accuracy, safety or policy compliance, graded on a spectrum rather than pass or fail, so teams can measure non-deterministic behaviour before and after release.
DataObserve Developer runs evals offline as a CI/CD quality gate and online against live traffic, bundling evals into suites with trajectory evaluation, safety and policy scoring, run comparison and an open-source SDK.
//Two modes, one loop
Score before you ship.Monitor after.
Offline and online evaluation share the same metrics and datasets, so there's no drift between what you test and what you measure.
Pre-deploy
Offline evaluation
Run agents against golden datasets before they ship, compare variants, and block the deploy if scores drop.
//Evaluate agents at scale
Evals bundle into suites. Suites gate your pipeline.
50+ scoring metrics
Faithfulness, relevance, tone, and task completion via LLM-as-judge, deterministic checks, and semantic similarity.
Trajectory evaluation
Multi-step behavior scored at the step, tool-call, and outcome level: not just whether it answered, but whether it reasoned.
Safety & policy scoring
Prompt-injection detection and policy-compliance scoring in every suite.
Run comparison
Diff any two runs with per-item deltas to catch regressions in PR or CI.
AI asset registry
Version prompts, agents, MCP tools, and skills; roll back or promote without a code deploy.
Open-source SDK
An Apache 2.0 evaluation SDK that runs in any CI system, notebook, or test suite.
//Explore more withDataObserve
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//FAQs
AI Evals: questions teams ask
What is an AI eval?
A score of an agent's output against a defined quality bar, graded on a spectrum rather than pass or fail.
What is a golden dataset?
Curated test cases: the inputs your agent will receive, with expected outputs or context. Your ground truth.
What is LLM-as-judge?
A language model grades a response against a rubric, scoring quality an exact-match assertion can't.
What is the difference between offline and online evaluation?
Offline evaluation scores an agent against curated test cases before release. Online evaluation scores real interactions after release, so quality is watched continuously.
What is trajectory evaluation?
Trajectory evaluation grades the steps an agent took, such as tool calls and reasoning, not only its final answer.
Can evals block a release?
Yes. Eval suites run in the pipeline and act as a quality gate, so a release that falls below the bar fails like a failed test.
