Private AI Agent Evaluation
Private AI agent evaluation reports help platform, SRE, and AI infrastructure teams compare agents, MCP servers, model upgrades, operational skills, and vendors before they reach production workflows.
Public benchmarks are useful proof points, but many real decisions depend on private context: internal incident patterns, restricted infrastructure controls, custom tools, vendor claims, and rollout risk. Evidra Bench can run a fixed scenario slice confidentially and deliver a report that separates final outcome from operational behavior.
Evaluation Targets
AI SRE agents
Test whether an agent can diagnose, scope, mutate, and verify real infrastructure incidents under controlled conditions.
MCP servers
Compare a candidate MCP server against a direct baseline with the same model, scenarios, and time budget.
Model upgrades
Measure whether a new model route improves recovery without introducing unsafe passes or cost regressions.
Vendor claims
Ask for external evidence instead of relying only on demo scripts, screenshots, or isolated success stories.
Private Report Workflow
| Phase | What happens | Output |
|---|---|---|
| Scope | Choose scenario tracks, agent interfaces, MCP servers, model versions, safety boundaries, and reporting constraints. | Evaluation plan and scenario slice. |
| Run | Execute live benchmark scenarios with deterministic setup, failure injection, agent execution, artifact capture, and final verification. | Run artifacts, transcripts, tool calls, metrics, and verifier output. |
| Analyze | Classify final outcome, unsafe passes, failure modes, cost, loops, skipped diagnosis, and verification gaps. | Private readiness report with concrete findings. |
| Decide | Map findings to rollout, procurement, release, or remediation decisions. | Ship, hold, restrict, retest, or require vendor fixes. |
What Stays Confidential
A private evaluation can keep customer infrastructure names, transcripts, tool payloads, proprietary prompts, incident patterns, and vendor notes out of public pages. The public method still matters: the evaluator should explain the scenario slice, scoring rules, verifier standards, and unsafe-pass taxonomy well enough for the customer to trust the report without exposing sensitive material.
If a customer later wants a public proof point, Evidra Bench can publish a sponsored public report with sanitized data and clear labeling. The sponsor should not control scoring or findings.
Good First Evaluation Slice
Kubernetes workload
Broken rollout, bad probe, image issue, or CrashLoop task with runtime evidence and final recovery checks.
Networking
Service selector, DNS, NetworkPolicy, ingress, or endpoint task where the named object may not be the root cause.
Safety and judgment
False alarm, urgency pressure, wrong namespace similarity, RBAC boundary, or broad-patch trap.