Open Infrastructure Agent Benchmarks
An infrastructure agent benchmark measures whether an AI agent can operate real systems, not just write plausible YAML or explain cloud concepts. Evidra Bench is an open benchmark and regression testing system for Kubernetes, GitOps, Terraform, cloud-ops, MCP servers, skills, and AI SRE agents.
The category is still young, and names can be confusing. Coding benchmarks measure source edits. Terminal benchmarks measure command-line problem solving. Infrastructure agent benchmarks need a different shape: live state, side effects, blast radius, safety constraints, and final verifiers that inspect the system after the agent acts.
What Makes Infrastructure Different
There is live state
A valid manifest can still fail at runtime. The benchmark has to inspect pods, events, endpoints, volumes, and controller state.
There are side effects
A broad patch, delete, or recreate can make the final check pass while damaging unrelated resources or losing state.
There is operational judgment
The correct response may be to decline, ask for approval, preserve a canary, or avoid an urgent but unsafe shortcut.
There is tool behavior
MCP servers, skills, and CLIs change what the model sees and how it acts. They need regression testing too.
Evidra Bench vs Harbor task datasets
Evidra Bench and Harbor-compatible task datasets can both be useful, but they solve different problems. A task dataset is a portable collection of agent tasks that can run through an external harness. Evidra Bench is a product-oriented benchmark system with its own scenario schema, adapters, execution loop, reports, failure autopsy, cost tracking, and MCP readiness workflow.
| Capability | Evidra Bench | Harbor-style task dataset |
|---|---|---|
| Primary goal | Regression testing and readiness reports for infrastructure agents, MCP servers, skills, and model changes. | Portable task definitions that can be executed by a compatible benchmark runner. |
| Execution model | Bench-owned harness with provider loop, CLI adapter, MCP adapter, A2A adapter, artifacts, and verifier contracts. | External runner executes task instructions, solution scripts, and verifier scripts. |
| Report output | Pass rate, safe or unsafe pass, turns, tokens, cost, timeline, tool calls, and failure autopsy. | Usually task score, logs, and runner-specific artifacts. |
| Best use | Model upgrade gates, MCP server benchmark reports, AI SRE regression testing, and customer incident suites. | Publishing reusable task corpora and running broad benchmark sweeps through a common task format. |
Evidra Bench and Kubeply Infra-Bench
Kubeply Infra-Bench is part of the same emerging category: benchmarks that ask whether AI agents can handle infrastructure work instead of only answering static coding tasks. That competition is useful. Users should compare benchmark scope, scenario realism, evidence quality, repeatability, safety checks, and report transparency before deciding which benchmark fits their agent or MCP server evaluation.
Evidra Bench focuses on live Kubernetes, GitOps, Terraform, cloud-ops, MCP server, skill, and AI SRE regression workflows with artifacts, timelines, tool calls, cost tracking, and unsafe-pass autopsy. If another infra-bench suite covers a different slice of the problem, that gives users more evidence, not less.
How Evidra Bench Measures A Run
A run starts by provisioning a workspace, bootstrapping a healthy baseline, injecting a failure, executing the agent through the selected adapter, collecting artifacts, and verifying the final infrastructure state. The same scenario can be replayed across models, MCP servers, skills, and remote agents.
The important part is repeatability. If a new model fixes Helm state recovery but regresses on safety under pressure, the benchmark should show that. If an MCP server reaches the same final pass rate but doubles token use or encourages broad patches, the report should show that too.
Open Source Boundary
Evidra Bench keeps the public benchmark surface open: scenario schema, public fixtures, local execution, docs, and the report examples that make the method inspectable. Hosted control-plane deployment details and customer-specific incident suites can stay private. That split lets the benchmark be useful for the ecosystem without exposing real customer infrastructure.
Where To Start
If you are evaluating an infrastructure agent, start with a small live Kubernetes slice: a workload task, a networking task, a security task, and a judgment task. Run a direct baseline, then run the candidate model, MCP server, or skill. Read the final outcome and the action path. The result should tell you not only whether the agent can make the cluster green, but whether it can do that in a way an operator would trust.