si: the execution layer for AI

we have been working on this for a while.

this did not start as a product idea. it started as frustration. we wanted AI systems to do real work, not just generate text, not just suggest code, not just simulate action, but actually operate in the real world.

we wanted AI to execute.

so we went back to first principles. what does it really mean for AI to act in the real world? what does it need? what is missing?

we tried the obvious paths first. MCP servers, gateways, vendor abstractions, orchestration layers that promised to connect everything. and the result was always the same. complexity.

there were too many moving parts, too much credential juggling, too many opaque bridges between the model and the outcome. nothing felt clean. nothing felt foundational. it felt like the industry was stacking adapters on top of adapters.

so we stepped back and asked a simpler question.

what already works?

APIs.

(controversial opinion alert!)

APIs have been the most stable interface in software for decades. they are explicit, deterministic, observable, auditable, and composable. while everyone was trying to reinvent the interface for AI native systems, we realized the interface was already there.

the real issue was not the API.

the real issue was execution.

models can reason. agents can plan. coders can generate code. but none of that guarantees outcome. intelligence without execution is incomplete.

there is a missing layer between intelligence and action, a layer that manages credentials safely, bridges AI systems to external services, provides structured execution surfaces, keeps everything deterministic, and stays simple.

that missing layer is what became si.

one of the biggest lessons we learned early is that credentials cannot be bolted on. you cannot expect agentic systems to manually juggle API keys, tokens, and secrets. that is fragile and dangerous. credentials have to live at the heart of the system.

that is why we built si vault.

vault handles API keys using a trust on first use model. credentials are scoped, stored securely, and injected intentionally. AI systems never need to directly manipulate raw secrets. as a result, AI can execute against real services without leaking, hardcoding, or mismanaging credentials.

once vault existed, the rest became obvious.

we did not want to spend our time wiring glue code. we actually do not like spending time on that. hopefully you do not either.

so we built a CLI from the ground up, a surface where AI systems can interact with providers, execute workflows, manage containers and runtimes, orchestrate structured tasks, and bridge into the outside world in a clean and predictable way without duct tape.

si is not another protocol experiment. it is not another layer of indirection. it is not trying to replace APIs. si embraces APIs and makes them usable by AI systems in a controlled, execution focused way.

the future of AI is not more prompts. it is reliable execution.

AI that can ship changes, trigger workflows, interact with services, manage state, and operate systems without constant human babysitting. that requires more than intelligence. it requires an execution layer.

si is that layer.

we are releasing si today, but this is not a polished final product. it is something we have been shaping carefully, testing against our own needs, and refining through friction.

now it is yours to try.

give us feedback. open issues. submit pull requests. give us a pat on the back. or curse us. seriously, tell us what works, tell us what breaks, tell us what is missing. push it. challenge it.

si is not finished. it is becoming.

jump on the train.

let us build the execution layer for AI together.

si the execution layer for AI.