Evals are the new CI for AI agents.
A purpose-built evaluation platform for AI agents and GenAI applications, covering offline evaluation before release and online evaluation in production. Use it on its own with the agent stack you already run, or as part of AgentOps.
Agents don't fail the way software fails.
They drift, hallucinate, misuse tools and regress silently with every prompt change, model update and retrieval config shift. "It worked in staging" means little for probabilistic systems that reason over changing context, tools and data. Without continuous evaluation, enterprises find quality failures only after their customers do.
Behaviour, safety and cost, on every change.
Does the agent complete tasks correctly across a representative set of inputs?
Does it refuse correctly and stay within its defined scope and constraints?
Did a prompt change significantly raise per-run token or compute cost?
Evidence before you ship, and after.
Agent Evals is an independent evaluation layer: separate from inference, aware of your agent's architecture and wired into CI/CD. Separating judgment from inference gives governed, reproducible and explainable scores you can defend in production.
Code, prompt edit, model swap or retrieval config, not just commits
Regression suites block anything that regresses before release
Ship with scores compared across providers and versions
Live traffic scored continuously; drift and failures alerted early
New scenarios flagged for human review and added to datasets
Today's production failure becomes tomorrow's regression test.
Built for agents, not just prompts.
Inference & judge pipelines
Independent pipelines for inference and judge LLMs, across models and providers, so experiments and routing don't couple eval quality to production inference.
Offline evals & regression testing
Built into CI/CD and triggered by any system-wide change, with regression reports across model providers and versions.
Execution-graph evaluation
Scores plans, tools and step transitions stage by stage across the agent's execution graph, finding the root cause at the step, not just the task.
Production monitoring
Continuous evaluation in production learns from real execution patterns and alerts on quality drift before users notice.
Configurable metrics & scoring
Opinionated defaults to start immediately; custom metrics and weights; comparable scores across every agent in the portfolio.
Closed-loop evaluation lifecycle
New production scenarios, edge cases and failure patterns are flagged for human review and added to eval datasets, so benchmarks keep pace with real use.
Reduce production risk
- Detect hallucinations and tool misuse early
- Prevent silent regressions before deployment
- Catch failures from non-code changes: model swaps, prompt edits, retrieval drift
Accelerate safe AI delivery
- Ship model, prompt and agent upgrades with confidence
- Faster experiments with measurable, comparable outcomes
- Evals become the quality gate, not the bottleneck
Enable enterprise governance
- One evaluation framework shared across AI teams
- Consistent quality gates and promotion criteria
- Audit-ready evaluation and decision traceability
On its own, or as part of AgentOps.
Agent Evals
Add an independent evaluation layer to the agents, pipelines and observability tools you already run. Nothing else to replace.
AgentOps
Evals as the release gate inside the full code-to-operate pipeline, alongside versioning, canary releases and instant rollback.
Explore AgentOps
How it connects to Operational Intelligence
Eval results are operational knowledge: what good looks like for each agent, where it failed, and why. Operational Intelligence captures them, so every regression found and every human review makes the next release safer.