Guides

Pick a starting point. Each guide is short and self-contained.

New: the value-prop variations (review)

Three short pages aimed at people who want AI agents that just work — no coding, no platform to learn. Same message, three different voices.

It just works → — Warm, plain-spoken. “You shouldn't have to learn how AI works.”
Done for you → — Confident, premium. “Pick the job. We deliver the agent.”
Bring us the task → — Bold, dark theme. “You don't need to learn AI. You need it to show up and do the work.”

You've got prompts, skills, and maybe a custom GPT or two. You want to make them work for your team.

For operators → — The pitch for people who've already built their own AI setup and want to deploy it.
How to build an AI agent → — A six-step walkthrough for turning a workflow you do every week into something your team can run.
Turn a skill into a runbook → — Already have a Claude skill or a custom GPT? Fastest path to making it reliable.

You're past the experimentation phase and trying to figure out how to scale this responsibly.

For teams → — Visibility, security, and vendor portability for the AI workflows your team relies on.
Model- and agent-agnostic → — Why your runbooks should outlive any one vendor's pricing decision.

You're shipping AI features and looking for the right primitive.

For builders → — Reliable agents, durable execution, OpenAI-compatible API, no framework to stand up.

The thinking behind how Jetty works.

What is a runbook? → — The format. Skills + standards = runbooks.
Jetty vs LangGraph and Managed Agents → — Honest comparison. When each is the right tool.

For wiring Jetty into your coding agent, in five minutes.

Install the agent skill → — Claude Code, Codex, Gemini CLI, Cursor, VS Code, Windsurf, Zed, or any MCP-compatible client.

For the API reference, agent configuration, sandbox snapshots, webhook payloads — everything past the conceptual material — see the developer docs.