I noticed OpenGradient has a tool called Veil sitting quietly in their GitHub — described as a local OpenAI-compatible proxy that keeps agentic prompts private and verifiable. I'm not completely sure how widely it's being used yet relative to the main SDK, but the framing caught me off guard. Most privacy tools in this space focus on user-facing applications. This one appears aimed specifically at developers running agent pipelines who don't want their intermediate reasoning steps exposed to third-party API providers during execution.
What seems interesting is the specific problem Veil seems to be responding to. When a LangGraph agent or a multi-step reasoning workflow calls an external LLM, the intermediate prompts — which can contain sensitive business logic, proprietary data, or user context — pass through whatever infrastructure the provider operates. A local proxy sitting in front of that flow and routing through OpenGradient's TEE layer means the orchestration layer stays on the developer's machine while the inference itself runs inside a verified enclave. The question that comes to mind is whether that separation actually holds under real agentic workloads, where prompt chains can get long, context windows fill up, and the boundary between local logic and remote inference blurs quickly.
Looking from the outside, Veil reads like OpenGradient trying to own a specific wedge in the developer stack — not the model, not the framework, but the trust layer sitting between an agent's reasoning and the compute it calls. It makes me think about how rarely infrastructure projects identify that kind of precise gap and build something narrow enough to fit it cleanly. Most try to replace the entire stack rather than insert one verified component.
I sometimes wonder whether developers actually reach for verifiability at the agent orchestration layer or whether they treat it as a deployment concern to solve later, long after the core architecture is already locked in — anyway, time will tell👍
#opg $OPG
What seems interesting is the specific problem Veil seems to be responding to. When a LangGraph agent or a multi-step reasoning workflow calls an external LLM, the intermediate prompts — which can contain sensitive business logic, proprietary data, or user context — pass through whatever infrastructure the provider operates. A local proxy sitting in front of that flow and routing through OpenGradient's TEE layer means the orchestration layer stays on the developer's machine while the inference itself runs inside a verified enclave. The question that comes to mind is whether that separation actually holds under real agentic workloads, where prompt chains can get long, context windows fill up, and the boundary between local logic and remote inference blurs quickly.
Looking from the outside, Veil reads like OpenGradient trying to own a specific wedge in the developer stack — not the model, not the framework, but the trust layer sitting between an agent's reasoning and the compute it calls. It makes me think about how rarely infrastructure projects identify that kind of precise gap and build something narrow enough to fit it cleanly. Most try to replace the entire stack rather than insert one verified component.
I sometimes wonder whether developers actually reach for verifiability at the agent orchestration layer or whether they treat it as a deployment concern to solve later, long after the core architecture is already locked in — anyway, time will tell👍
#opg $OPG