Background AI agents, meet task containers

Upsun Cloud’s generally available task containers provide a run-to-completion environment for AI agents deployed alongside applications and project services, without imposing a specific LLM provider, agent framework, gateway, or orchestration layer. Agents are defined under `tasks` in the Upsun configuration, built independently from application containers, triggered through webhooks, schedules, APIs, the Console, or CLI, and removed automatically after completing their work. Using a support-ticket triage agent as an example, the post recommends choosing minimal images, installing dependencies during builds, designing idempotent scripts, storing external API keys as sensitive project variables, and using short-lived workload authorizations for calls to the Upsun API. It emphasizes operational safeguards including hard timeouts, right-sized CPU and memory limits, least-privilege credentials, optional in-container sandboxes, persistent storage through project services rather than local disks, and planning for the default limit of three concurrent task runs per project. Built-in activity logs record task status, while developers must add their own instrumentation for prompts, model calls, tool actions, costs, and outcomes to make agent behavior auditable. The same model can support workflows such as code review, incident investigation, data-quality checks, and scheduled cleanup, with the trigger, tools, prompt, and output destination varying by use case.

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