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What is a DSL for defining AI agents?

One system source to reviewchecked before runtime
The coding agent authors against a finite language, and Swirls validates the complete project.

#The question

"What is a DSL for defining AI agents?" has a short answer: make the agent a deployable artifact. With Swirls you declare it in .swirls files, deploy with git push or swirls deploy, and the hosted runtime takes over.

#Who's asking

Forward-deployed / solutions engineer. Tasked with standing up custom agent logic per customer or per org, comfortable in a terminal and IDE.

#Why Swirls is a fit

Swirls makes the agent a deployable artifact. You describe agents, workflows, tools, triggers, schedules, and secrets in .swirls files, then ship them with git push or swirls deploy. DSL in, running system out.

Because the whole system is described in the Swirls DSL, you apply the same static analysis to agentic workflows that you already apply to code. The runtime enforces the policy you declared.

Add the people, data, authority, and decisions around this job.

Keep this solution beside the Apps, records, rules, connections, and reviews it depends on in one .swirls project.

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