Agent evaluation platform
Evaluate complete agentic AI apps
GENEVAL helps teams test, trace, and measure the reliability of LLM and agentic AI applications end to end. Evaluate workflows, compare versions, inspect failures, and monitor quality signals before and after release.
Built for
LLM products, agentic systems, and enterprise teams that need measurable quality across full application workflows.
Core value
End-to-end evaluation, tracing, and release confidence without stitching together fragmented testing and observability tools.
Eval
Test complete tasks, multi-step flows, and agent outcomes before release.
Trace
Inspect execution paths, tool calls, and failure points across live agent runs.
Why GENEVAL
A quality layer for agentic AI reliability
From offline evals to production tracing, GENEVAL gives teams a single operating layer to measure quality, investigate regressions, and make safer rollout decisions for LLM and agentic applications.
End-to-end testing
Run scenario-based evaluations across full user journeys, multi-turn conversations, and agent workflows instead of isolated prompt checks.
Tracing and debugging
Trace agent decisions, tool usage, retrieval steps, and response paths so teams can pinpoint why failures happen and where reliability breaks down.
Version comparison
Compare prompts, models, tools, and orchestration changes side by side to understand quality tradeoffs before rollout.
Production quality signals
Monitor drift, failure patterns, and reliability metrics in production so quality stays visible as usage and complexity scale.
How it works
A practical workflow for agent evaluation
GENEVAL supports a disciplined loop: define success criteria, run end-to-end evals, inspect traces, compare changes, and monitor live behavior after deployment.
1. Define eval criteria
Set tasks, datasets, rubrics, and pass thresholds that reflect what good performance means for your application.
2. Run end-to-end tests
Evaluate prompts, models, retrieval, tools, and orchestration together to surface regressions across full workflows.
3. Inspect traces
Review execution traces and failure paths to understand where agent behavior, tools, or context handling break down.
4. Monitor reliability
Track live quality signals continuously so teams can respond faster as traffic, tasks, and agent complexity grow.
Enterprise ready
Built for teams shipping agentic AI in production
Deepsoft AI positions GENEVAL for organizations that need dependable rollout decisions, stronger debugging workflows, and measurable quality standards for LLM and agentic applications.
Reliable evaluation
Create repeatable standards for measuring quality across prompts, models, tools, and agent workflows.
Deep visibility
Understand system behavior with traces, comparisons, and production insight that speed up debugging and iteration.
Safer releases
Support stronger governance and rollout discipline as agentic systems move from pilot to enterprise scale.