We have been working on this for a while.
si did not start as a product pitch. 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.
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?
The problem we kept running into
We tried the obvious paths first. MCP servers, gateways, vendor abstractions, orchestration layers that promised to connect everything.
The result was always the same: complexity.
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 the industry keeps trying to reinvent the interface layer for AI native systems, we realized the interface was already there.
The real issue was not the API.
The real issue was execution.
Intelligence without execution is incomplete
Models can reason. Agents can plan. Coders can generate code.
But none of that guarantees outcome.
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, and keeps everything predictable.
That missing layer is what became si.
Credentials cannot be an afterthought
One of the biggest lessons we learned early is that credentials cannot be bolted on.
You cannot expect agentic systems to juggle raw 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.
What si actually is
si is a CLI built from the ground up as an execution surface for AI systems.
It allows AI agents, coders, and automation workflows to interact with APIs and external services in a controlled and structured way. Instead of reinventing transport layers or wrapping everything in another protocol abstraction, si embraces APIs and makes them usable by AI systems safely and predictably.
It is not another gateway experiment.
It is not another orchestration wrapper.
It is not trying to replace APIs.
It is the layer that connects intelligence to action.
Why this matters
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 requires more than reasoning. It requires an execution layer.
si is that layer.
The repo
The project is open source and available here:
We have been shaping it against our own real world use cases, refining it through friction, and building it in the open.
This is just the beginning.
If this resonates, try it. Break it. Open issues. Submit pull requests. Tell us what works. Tell us what does not. Tell us what you would design differently.
We are committed to building something that feels missing in today’s AI ecosystem.
si is not finished. It is evolving.
Let us build the execution layer for AI together.
Top comments (0)