USE CASES

What it catches, and how it resolves.

Five scenarios -- each one reproducible. Real commands, deterministic output, public artifacts, no invented customers. Run any of them yourself in five minutes.

SHADOW AI

An AI agent was running in the cluster. Nothing was tracking it.

A platform team runs a kagent fleet on Kubernetes. Someone else shipped a LangGraph agent as a plain Deployment -- no governance label, not in any inventory, with a model API key and cluster read access. A label-based tool sees nothing. Valqore found it on the first scan, with zero opt-in.

A major vendor's reference AI-agent deployment: 92/100 security, 28 governance gaps.

Microsoft publishes an 'AI Agents for IT/Ops' workshop -- a real .NET agent on AKS, the kind thousands of teams copy into production. We ran its manifests through Valqore, unedited. The security work is genuinely strong. The governance posture is essentially absent. That gap is the category, not the vendor.

92/100SECURITY POSTURE

An incident agent read a runbook that said “roll back the api deploy.” The gate held it for a human.

Runbook-reading agents retrieve the right fix during an incident -- genuinely useful. They're also one wire away from danger: the retrieved runbook ends with a destructive step. The instant an agent can act on what it reads, that recommendation becomes an action that needs a deterministic gate, not the model's judgment.

read-onlyALLOWED INLINE

destructiveHELD FOR HUMAN

HMACSIGNED JOURNAL

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RUNTIME GUARDRAILS

The manifest looked fine. The telemetry showed 1,500 steps and a $6,752/mo run-rate.

An annotation can claim a step budget. Valqore doesn't take the claim on faith -- it checks the Prometheus/OpenTelemetry counters the agent already emits against what it declared. This one declared nothing and ran hot: 1,500 reasoning steps, 240 tool calls, 1.85M tokens in the last window.

1,500STEPS OBSERVED

0DECLARED CAP

$6,752/moPRICED RUN-RATE

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CONVERGED COVERAGE

A point scanner flagged 62 IaC misconfigs. It can't see cost, carbon, AI governance, or compliance.

Checkov is good at what it does -- security and IaC misconfiguration. On the same corpus it returns 62 failed checks. What it structurally cannot return is the cost, carbon, AI-governance, and compliance picture, or a single verdict. Valqore catches the same misconfigs and the rest, in one pass.

1,379RULES, ONE ENGINE

4+DOMAINS POINT TOOLS MISS

1SCORE + VERDICT

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Pick the scenario that fits your stack.

Each one is a runnable proof against a public artifact -- no signup, no demo data. Or talk to us about a design-partner pilot.

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