Running AI Workloads

This page describes the emergent patterns in how AI workloads function and the best way to author, run, investigate and improve their quality.

Can Jetty help me solve this task?

An AI workload is an automation-ready task that requires human judgement and can be meaningfully measured by an expert. Before delegating the job to Jetty, ask yourself: can you define what a good result looks like, and will the task come up again? If the answer is yes to both, then this is a good candidate for running it on Jetty.

Repeatable work with no real quality bar is basic automation. You may want to benchmark a basic automation with deterministic scripts against an AI workload. For instance, processing hundreds of documents is a good example of an AI workload, whereas a back-and-forth with a chatbot might be best done with another tool.

Jetty runs plain language instructions, called runbooks, in a virtual machine called a sandbox with a coding agent.

Runbooks: the agent workflow description

A Jetty runbook is a markdown file that provides an AI agent all the information required to perform a long-running task to a pre-defined standard of quality. While it might be tempting to think of runbooks as just fancy prompts, there's a little more going on under the hood.

If you haven't installed Jetty yet, start with Bring your own agent: the plugin, the jetty-setup skill, and your first run.

The same deployed runbook can be re-run with a different model, agent, or input without editing the file, which is what makes model and agent sweeps a loop instead of a project. The exact workflow shape and the parameter-resolution rules are in the machine instructions.

Runtime configuration

The runtime configuration characterizes which model and agent invoke the runbook, the environment they are given, and the tools they have access to. Each runtime's provider compatibility, model id format, and a configuration that runs are in Runtime Configuration.

Bring Your Own AI Key (Optional)

Runbooks can also use an AI Gateway of your choosing: name a model and a model_provider and Jetty routes accordingly. By default, we use an OpenRouter multi-provider gateway. Learn more about setting up your own AI Gateway.

Provider keys are configured for your whole account (Settings → Bring Your Own Key) and injected into the sandbox at run time.

The same endpoint doubles as a plain LLM proxy with a chat-completions call without a runbook attached is passthrough mode, routed across 100+ providers, with every call recorded as a run.

Tools, skills, and MCPs

The agent runtime brings its own tools as part of the runbook. This includes file editing, shell, web navigation etc. You choose it with the agent: frontmatter field of the runbook.

Runbook tasks can also attach MCP servers. These can be declared as either a local command or a remote HTTP endpoint. See more details in the machine instructions.

Skills go inside runbooks since they are already plain markdown.

Integrations

Your AI Workload will probably need to connect to some system or push data into something downstream. Fortunately, agents are clever enough, given the right integration details, to connect appropriately. Each workload manages its own integrations in the form of environment variables defined as part of the runbook. Learn more about setting up integrations and credentials.