Background Agent
An agent run that continues without a person watching, started by a schedule, an event, or a handoff, and reports its results when done.
A background agent is an agent run that proceeds without a person watching each step. Instead of an interactive session, where someone reads every tool call and can interrupt, the agent is given a task and left to work, on a server, in a cloud sandbox, or as a scheduled job. It reports back when finished or blocked: with a pull request, a summary, a notification, or a question. The Agentic Loop is the same. What changes is everything around it.
Runs start in a few ways. A person can hand off a task ("fix this and open a PR"). A schedule, the Cron model, can run a recurring task such as a nightly dependency check. An event such as a Webhook from an issue tracker or a failed build can trigger one. Several background agents can run in parallel, which is where Multi-Agent Orchestration and Fan-Out and Fan-In patterns come in.
Removing the watcher removes a safety layer, so the design work shifts to the edges:
- Permissions. An unattended agent should hold only the access the task needs, inside a sandbox such as a container with scoped credentials. See Least Privilege.
- Checkpoints and approvals. Irreversible or outward-facing actions, such as merging, deploying, or messaging people, belong behind a Human in the Loop gate rather than at the agent's discretion.
- Untrusted input. Background agents often read issues, emails, or web pages written by strangers, making Prompt Injection a primary risk rather than an edge case.
- Budgets. With no one to notice a loop, runs need hard limits on time, steps, and spending. See Token Budget.
- Durability. Long runs fail midway. Persisting progress, as durable execution engines do, lets a run resume instead of restarting, and Idempotence keeps retried steps from doing things twice.
- Reporting. The final report is the only part a person may read, so it must stand on its own, saying what was done, what was not, and what is uncertain, without overstating success.
Observability matters more than for interactive use. A transcript, tool-call log, and diff let someone audit a run after the fact, which is the background equivalent of watching it happen.
See also1
Linked from4
- Agent SandboxAgents & Language Models
Confining an agent's commands to a limited environment, so that a mistaken or hijacked action can only damage what the sandbox allows it to reach.
- Agentic LoopAgents & Language Models
The cycle of model turn, tool call, observation, repeat — the basic mechanism of an AI agent.
- CronSystems & Tooling
Time-based scheduling of recurring jobs, and the reasons scheduled jobs quietly stop working.
- Human in the LoopAgents & Language Models
Requiring a person's judgment at chosen points in an otherwise automated process.