Linear Loops Turns Recurring Work into Agent Workflows

Linear Loops shows how product systems are starting to embed agents not as chat, but as repeatable workflows with schedules, events, run memory, and context access.

Jul 21, 20261 min1 source

In one minute

Linear Loops shows how product systems are starting to embed agents not as chat, but as repeatable workflows with schedules, events, run memory, and context access.
The record is connected to 3 topics: linear, agent-workflows, product-ops.
1 public source carries the evidence boundary.

Source ledger

Publishable sources attached to this record.

1 public source

#	Source	Role	Public status
1	linear.appsource	primary receipt	`source_urls`

Linear Loops is important because the agent appears not as a separate chatbot, but as part of the product operating system. A loop can run on a schedule or event, work with workspace context, and return to repeated tasks.

This is closer to an agent workflow than an AI assistant. A loop has context, a trigger, run history, integrations, and an expectation of repeatable output.

What changed

Product teams gain new operating units:

recurring investigation;
automatic triage;
task follow-up;
links between Linear, codebases, and MCP;
loops that live longer than one chat turn.

New Runtime Read

When an agent gets a schedule and memory of previous runs, it becomes a small work process. This is no longer “ask AI”. It is “put AI on duty”.