Build your own Claw.
Give it a goal, a memory, a schedule, a message channel, and a queue of work. It keeps a record of what it did. It brings you in when it needs a decision. The core of that loop is 24 lines.
You describe the work that you want done. An AI writes the workflow. WhippleScript makes sure that the workflow is safe to run. You do not write code.
That is why your IT department says no. WhippleScript answers the question with a compiler. It checks each workflow against the rules that your organization already set, before the workflow runs. Your IT department can now say yes.
Give it a goal, a memory, a schedule, a message channel, and a queue of work. It keeps a record of what it did. It brings you in when it needs a decision. The core of that loop is 24 lines.
It reads a request, collects the documents, drafts a response, and routes the work. Nothing goes out until you approve it.
It researches, runs the comparison, and continues until it reaches an answer or a stopping point that you set.
The useful workflows combine private records, the open internet, financial systems, internal documents, external messages, and language models. Each combination is a place where an automation can do damage.
Ask for a workflow that reads a customer account, checks current information on the web, prepares a payment request, and asks you before money moves or a message goes out.
Before the workflow runs, WhippleScript checks the connections. It tells you when private information would reach the wrong model. It tells you when information from the web or an inbox could steer an important action. It tells you when the workflow needs a person to decide.
The second objection belongs to the person who approves your tools. Send that person one page. It states what the compiler refuses to build, and it names the evidence for each refusal.
“Can I let my team build their own AI automations without reviewing every one of them?”