Kubernetes failure handling
Evaluate whether an agent can inspect workloads, identify crash loops, reason about manifests, and propose safe remediation steps.
Evidra Bench: external regression testing for AI infrastructure agents and MCP tools
Run AI infrastructure agents and MCP tools against Kubernetes, Terraform, Helm, Argo CD, and AWS/LocalStack-style scenarios before trusting them in production workflows.
AI infrastructure agent regression testing
Benchmarks for agents that operate infrastructure
Evidra Bench focuses on external, repeatable evaluation instead of one-off demos. It helps teams see whether an agent can handle failure signals, tool constraints, and operational tradeoffs when the environment is not shaped around the happy path.
Kubernetes
evaluation surface
Terraform
evaluation surface
Helm
evaluation surface
Argo CD
evaluation surface
AWS/LocalStack
evaluation surface
MCP
evaluation surface
Evaluation scenarios
Kubernetes, Terraform, Helm, Argo CD, and AWS/LocalStack scenarios
The benchmark surface is designed for infrastructure automation that has to diagnose, plan, and act under constraints. It covers agent behavior, MCP tool behavior, and the evidence needed to compare changes over time.
Terraform plan review
Measure how automation handles drift, unsafe plans, provider errors, imports, and infrastructure-as-code reasoning under realistic constraints.
MCP tool evaluation
Test MCP tools and agent workflows that call infrastructure systems, require credentials, or need repeatable local execution.
Operational regression testing
Compare behavior across model, prompt, tool, and skill changes before expanding trust in production automation.
MCP tool evaluation
From submission to benchmark report
1
Submit
Share the product URL, tool type, evaluation surface, local run support, and report preference.
2
Run
Evidra Bench exercises the agent or MCP tool against realistic infrastructure scenarios.
3
Compare
Results are reviewed across failure handling, evidence quality, constraints, and repeatability.
4
Report
Choose a public benchmark note or a private report for internal engineering review.
Building an AI infra agent or MCP tool?
Submit it for an early live evaluation. First 3 design partners get discounted private reports.
Evidra Bench FAQ
Practical details for teams evaluating infrastructure agents, platform copilots, and MCP tools.
What is Evidra Bench?
Evidra Bench is an external regression testing system for AI infrastructure agents and MCP tools. It evaluates behavior against realistic Kubernetes, Terraform, Helm, Argo CD, and AWS/LocalStack scenarios.
Who is Evidra Bench for?
It is built for teams shipping infrastructure agents, MCP tools, developer automation, platform copilots, and AI workflows that operate production-like systems.
Why use external agent evaluation?
External evaluation reduces overfitting to demos. It gives teams repeatable evidence about how an agent behaves when prompts, models, tools, and runbooks change.
Can Evidra Bench produce private reports?
Yes. Early design partners can request private reports for internal review, while public reports can help vendors show benchmark visibility.