One of the persistent frustrations with AI agents in production is that nobody agrees on how to know if they’re working correctly. Solo.io is taking a shot at solving that with agentevals, an open-source project launched at KubeCon + CloudNativeCon Europe 2026 in Amsterdam.

The premise is straightforward but the execution is non-trivial: continuously score your agents’ behavior against defined benchmarks, using your existing observability data, across any LLM model or framework. Not a one-time evaluation. Not a test suite that only runs before deployment. A live, ongoing signal.

What agentevals Does

At its core, agentevals provides a pluggable evaluation layer that sits alongside your production agent infrastructure. You define what “good” behavior looks like for a given agent task — accuracy on tool selection, adherence to output formats, latency under load, whatever matters for your use case — and agentevals continuously scores actual agent runs against those benchmarks.

Key design decisions that differentiate it from existing eval frameworks:

  • Model-agnostic: Works with any underlying LLM or agent framework — you’re not locked into evaluating only Solo.io-native agents
  • Observability-native: Pulls from your existing observability stack rather than requiring a separate data pipeline
  • Production-focused: Designed for ongoing monitoring, not just pre-deployment testing

This matters because the gap between “agent works in testing” and “agent works reliably in production” is enormous. Eval frameworks that only run at deployment time catch regressions when you cut a release — but agentic behavior degrades in subtler ways: prompt drift, model provider changes, real-world input distributions that don’t match your test sets.

The agentregistry CNCF Contribution

Alongside agentevals, Solo.io is contributing agentregistry to CNCF — a lifecycle management system for AI agents, MCP tools, and Agent Skills. Think of it as a package registry for agentic components: discovery, versioning, dependency tracking, and integration with the broader Kubernetes ecosystem.

The agentregistry contribution is strategically interesting. By pushing this into the CNCF namespace, Solo.io is making a bet that agent lifecycle management becomes a cloud-native concern — something that platform engineering teams manage alongside container registries, Helm charts, and service meshes. If that bet pays off, agentregistry becomes the standard, not a proprietary Solo.io product.

KubeCon 2026 as a Turning Point

The combination of announcements coming out of KubeCon Europe 2026 — Dapr Agents v1.0 GA, agentevals, and the CNCF AI Conformance Program expansion — signals something important: the cloud-native ecosystem is treating agentic AI as production infrastructure, not research infrastructure.

That means reliability, observability, and lifecycle management are becoming first-class concerns. agentevals and agentregistry are Solo.io’s contribution to that emerging stack.

Getting Started

agentevals is open source and available now. The agentregistry CNCF contribution is underway — expect formal CNCF sandbox/incubating status to follow. Solo.io documentation and GitHub repos are the starting point for teams wanting to evaluate either project.

Sources

  1. GlobeNewswire: Solo.io introduces agentevals at KubeCon Europe
  2. Yahoo Finance: Solo.io agentregistry CNCF contribution
  3. CNCF: KubeCon Europe 2026

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260325-0800

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