Problem

AI infrastructure agents can pass simple prompts while failing realistic Kubernetes, Helm, Argo CD, Terraform, and cloud scenarios in ways that are hard to reproduce.

Approach

Evidra Bench runs infrastructure agents and MCP tools against realistic Kubernetes, Helm, Argo CD, Terraform, and AWS/LocalStack scenarios, then turns results into repeatable regression signals.

Outcomes

• Repeatable evidence for agent capability, failure modes, and release readiness.
• Lower risk when changing prompts, tools, models, policies, or agent runtimes.

Problem

Production drift and unclear change origin in Argo CD rollouts create delayed investigations and weak audit narratives.

Approach

Evidra generates structured evidence from Argo CD history and exportable artifacts for incident response and compliance review.

Outcomes

• Faster investigation handoffs across operations, security, and compliance teams.
• Audit-ready change narratives built from consistent deployment evidence.

Problem

AI agents proposing Terraform or Kubernetes changes without deterministic validation introduces high operational risk.

Approach

Evidra intercepts tool calls, applies deterministic OPA/Rego policies on infrastructure diffs, and records a hash-linked evidence chain.

Outcomes

• Controlled infrastructure automation with policy checks before execution.
• Deterministic safety for probabilistic AI-generated change proposals.

Problem

Automation flows bypassing formal approvals can violate policy and increase operational risk.

Approach

Evidra policies encode approved runbooks, permit only whitelisted operations, and append evidence for every execution.

Outcomes

• Safer automation with explicit, enforceable policy boundaries.
• Clear run-level evidence for internal and external review.

Problem

Post-incident evidence is scattered across deployment, automation, and platform systems, delaying root-cause analysis.

Approach

Combine Evidra deployment evidence with Evidra operation evidence to build coherent, time-aligned incident narratives.

Outcomes

• Consistent evidence artifacts for postmortems and audit reviews.
• Lower coordination overhead during incident response timelines.

Problem

MLflow environments need controlled access patterns, secure connectivity, and production-grade hardening.

Approach

Apply MLflow Enterprise Gateway deployment patterns and advisory for network boundaries, operational controls, and rollout planning.

Outcomes

• Stronger production posture for enterprise ML platform operations.
• More predictable deployment and change-management practices.

Problem

Governance rules living only in documentation are bypassed under delivery pressure.

Approach

Convert governance rules into executable OPA/Rego policy gates with a default-deny posture before automation runs.

Outcomes

• Enforceable governance with less dependence on manual process checks.
• Predictable automation behavior aligned with platform policy.

Problem

Auditors require exportable evidence artifacts and retention controls that are hard to assemble manually.

Approach

Use Evidra exportable artifacts and Evidra tamper-evident chains, plus advisory for retention policy and review workflows.

Outcomes

• Structured compliance workflows without manual evidence reconstruction.
• Improved confidence in evidence provenance and continuity.

Problem

Teams want faster delivery but need to avoid quality regressions and uncontrolled change paths.

Approach

Advisory and implementation across CI/CD guardrails, GitOps evidence, policy gates, and observability controls.

Outcomes

• Faster delivery with controlled operational risk.
• Reviewable automation outcomes tied to evidence-backed workflows.