| Eval dimension | Score | Threshold | Trend | Status |
|---|---|---|---|---|
| Task successCompleted intended action | 96.2% | 95% | +1.8% | Pass |
| Answer relevanceGrounded response quality | 92.7% | 90% | +0.6% | Pass |
| Policy complianceSensitive-data and safety checks | 99.1% | 99% | 0.0% | Pass |
| Tool selectionCorrect API/tool used | 86.4% | 90% | -5.2% | Review |
A model can stay available while output quality, safety, relevance, or cost deteriorates. Traditional uptime monitoring cannot tell whether AI still creates value.
AnoSys connects continuous evals with model telemetry, token usage, latency, user behavior, and business outcomes so teams can catch regressions early.
Connect offline and production evals to the traces, model versions, prompts, users, and business outcomes they are supposed to protect.
Run quality, safety, relevance, policy, latency, cost, and custom evals by workflow.
Track behavior across prompts, models, releases, cohorts, customers, and provider changes.
Link regressions to traces, tool calls, model settings, data changes, and affected user segments.
Use eval evidence to trigger alerts, release reviews, owner handoffs, and remediation workflows.
Yes. AnoSys supports OpenAI, Anthropic, Claude Code, custom LLM integrations, and OpenTelemetry-based instrumentation.
Yes. AnoSys supports production monitoring and continuous evaluation so regressions are detected after deployment, not only in test suites.