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Show HN: OQP – A verification protocol for AI agents (github.com/oranproai)
6 points by Aamir21 19 hours ago | hide | past | favorite | 3 comments
As AI agents autonomously write and deploy code, there's no standard for verifying that what they shipped actually satisfies business requirements. OQP is an attempt to define that standard.

It's MCP-compatible and defines four core endpoints: - GET /capabilities — what can this agent verify? - GET /context/workflows — what are the business rules for this workflow? - POST /verification/execute — run a verification workflow - POST /verification/assess-risk — what is the risk of this change?

The analogy we keep coming back to: what OpenAPI did for REST APIs, OQP does for agentic software verification.

Early contributors include Philip Lew (XBOSoft) and Benjamin Young (W3C JSON-LD Working Group). Looking for feedback from engineers building on top of MCP, agent orchestration frameworks, or anyone who has felt the pain of "the agent shipped something wrong and we had no way to catch it."

Repo: github.com/OranproAi/open-qa-protocol

 help



This looks pretty interesting. The idea of having a verification layer for agent-generated code makes sense as things get more automated. One thing I’ve noticed with LLM systems is that even when things pass tests, they can still behave unpredictably at runtime. Curious if OQP is mostly focused on pre-merge verification, or if you’re thinking about handling runtime behavior as well?

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@aayushkumar, please give us a star on github if you like the work.

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A few questions..

1. Does the /verification/assess-risk endpoint capture what you'd actually need in a CI/CD gate? What's missing from the request/response schema? 2. The Knowledge Graph as the source of business rules, does this model make sense for your stack, or is there a simpler primitive we should define first? 3. MCP compatibility, are there gaps in how OQP maps to MCP's tool/resource model?

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