Use case ยท AI-native software
Verify the behavior behind AI-native software.
For teams shipping agents, tool-calling workflows, AI-generated code, and compliance-sensitive AI features where intent can drift faster than review can follow.
Agents / AI code / tool calls / feature reviewThe approved behavior lives across docs, prompts, tickets, tests, and senior engineer memory.
AI-assisted changes increase surface area and review volume.
Proof identifies gaps before the feature becomes customer-visible.
What Proof reviews
AI features fail when intent is underspecified.
Tool calls, retries, policy checks, state transitions, and failure handling.
Implementation gaps, missing requirements, and tests that only mirror generated behavior.
Customer-facing claims, security review answers, compliance-sensitive behavior, and release notes.
Where it starts
One workflow. One set of claims. One evidence packet.
Proof can review planned or in-progress features before release, but the first engagement stays concrete: one critical behavior path, the evidence supporting it, and the gaps that could surprise customers.
- Start with one agent workflow or AI-assisted feature.
- Map intended behavior against implementation and tests.
- Deliver findings, reproducers, and evidence gaps.
- Optionally turn artifacts into CI/CD release checks.
Starting point
Bring one AI workflow that customers depend on.
- Agent loops, tool calls, retries, policy checks, and state transitions.
- AI-assisted code changes where tests may mirror generated behavior.
- Customer-facing claims that need evidence before enterprise review.
- Feature work that needs implementation-gap review before release.
Do not submit secrets or private source code here. See Trust.