Workbenches

Overview

Workbenches are named, project-scoped environments for running AI-driven operations against your infrastructure. Each workbench bundles a system prompt, a set of capabilities, connected tools, and automation triggers into a reusable workspace that your team can run on-demand, on a schedule, or in response to incidents.

At runtime, a workbench job is created — the agent executes using the configured capabilities and tools, emitting a live stream of activities as it works. When finished it produces a structured conclusion that can include summaries, dashboards, follow-up todos, topology pointers, and opened pull requests.

Key things you can do with a workbench:

• Run an AI agent against your Plural-managed services, stacks, and Kubernetes clusters — respecting your existing RBAC
• Connect external tools like Datadog, Prometheus, Elasticsearch, Slack, GitHub, and custom HTTP APIs so the agent has full operational context
• Trigger jobs automatically from observability alerts, issue trackers, or cron schedules

Core concepts

Workbench

The parent configuration object. It defines the agent's identity (name, system prompt), the project it belongs to, the agent runtime to use, which capabilities are enabled, which tools are attached, and who has access.

Workbench job

A single run of the agent against a prompt. Jobs are created manually in the UI, by a cron schedule, by a webhook trigger, or from a Plural Flow. Each job has a status (pending, running, complete, failed) and a streaming activity log you can follow in real time.