01 · Orient
Start with the operational picture
The dashboard groups the system into Provisioning and Runtime. Users, roles, artifact storage, jobs, schedules, executions, and workers are visible as connected parts of one operating surface.
Inside Laibor
Put AI to work, end to end.
Laibor is an AI-enabled workflow-driven job runner for designing, scheduling, running, and inspecting observable work.
02 · Design
Compose a job as a workflow graph
A job is a durable definition made from capability-backed tasks. Tasks can branch, declare dependencies, and pass shared context forward, so the workflow describes how work moves instead of hiding it inside a script.
03 · Adapt
Use the workspace your way
Laibor supports two themes: Light and Dark. The interface keeps the graph, task states, and actions legible while you design and inspect workflows in the environment that suits you.
04 · Run
Turn definitions into executions
Run a job immediately or schedule it once, repeatedly, or on an interval. Each execution records its own status, outcome, timestamps, inputs, context, telemetry, errors, and artifacts.
05 · Observe
Follow the work as it happens
Logs preserve ordered worker and task diagnostics, including runtime events, standard error, parse failures, and task failures. The JSON and JSONPath filter makes a long execution trace easier to search.
06 · Deliver
Keep the result attached to the run
Artifacts are first-class outputs with metadata and provenance. That gives operators a durable trail from a result back to the task and execution that produced it.
07 · Share
Meet teams where they are
Laibor supports four languages: English, Spanish, French, and German. The Spanish jobs surface shown here is one example of a console that remains understandable as teams, workflows, and environments grow.