Evidra Bench early evaluation

Building an AI infra agent or MCP tool?

Submit it for an early live evaluation.

First 3 design partners get discounted private reports.

Scope

Kubernetes, Terraform, and MCP evaluation surfaces.

Run mode

Local runs preferred; hosted or API-backed flows can still be reviewed.

Output

Choose a public benchmark note or a private report for your team.

Submit your agent/tool

Share the minimum details needed to decide whether your agent or tool fits the first evaluation cohort.

Kubernetes / Terraform / MCP?
Do you support local run?
Can you provide API keys / usage budget?
Public or private report?
AI infrastructure agent benchmark

Evidra Bench evaluation for AI infrastructure agents

Evidra Bench provides external regression testing for infrastructure agents and MCP tools that operate real systems. The goal is to measure behavior across Kubernetes, Terraform, Helm, Argo CD, and AWS/LocalStack-style scenarios before teams trust an agent in production workflows.

Kubernetes remediation agents

Evaluate how agents investigate pods, deployments, crash loops, configuration changes, and operational failure signals.

Terraform planning and drift workflows

Test plan review, unsafe change detection, drift handling, provider errors, and infrastructure-as-code reasoning.

MCP tools that operate infrastructure systems

Review tool invocation behavior, execution constraints, credential requirements, and evidence quality for MCP workflows.

Evidra Bench FAQ

Practical details for teams submitting AI infrastructure agents, MCP tools, and automation systems for early evaluation.

What can I submit to Evidra Bench?

You can submit AI infrastructure agents, Kubernetes automation, Terraform workflows, MCP tools, or developer tools that operate infrastructure systems.

What does the live evaluation cover?

The evaluation focuses on realistic infrastructure scenarios such as Kubernetes failures, Terraform planning, MCP tool calls, local run support, and API or usage budget constraints.

Can the report stay private?

Yes. You can request a private report for internal review, or choose a public report if you want benchmark visibility.

Do I need to support local execution?

Local execution is preferred because it makes evaluation repeatable, but hosted or API-backed tools can still be reviewed when credentials and budget limits are clear.