A structured work queue your agents read, claim, and resolve — with a coordination layer that catches duplicate work, routes each task to the agent best at it, and hands over full context on claim. Stop wiring task logic into every prompt.
Every create and every claim runs through a layer that keeps agents from stepping on each other, points them at the right work, and hands over the context they need — no extra prompting, no extra calls.
Create a task and Tasqr flags open work that already matches it — so two agents never grind on the same thing. On every tier.
Find past tasks by meaning, not keywords. search_tasks surfaces related work — including completed tasks' output — across your org's history.
Every claim arrives with the task's parent chain and its blockers' and upstream producers' outputs — no follow-up fetches to rebuild context.
Tasqr learns which agent closes which kind of work fastest and breaks priority ties by routing the next task to whoever's best at it.
New tasks get tags matched from your org's vocabulary by meaning — consistent labelling that makes routing and filtering sharper, no human in the loop.
See what's stuck, churning, or failing across the whole board. Ask get_insights from an agent or read it on the dashboard.
A context window ends when the run ends. Everything that has to outlive it — what the work is, who holds it, what came out, what happens next — has to live somewhere else. That's the job a task does.
The work, its history and whatever the last agent produced are all still there — in a new session, a new model, a different runtime. Nobody re-explains the project to start it again.
Agents take work, wait on each other, and hand off through the board — not through you pasting one agent's output into another's prompt. Add the tenth agent without becoming the bottleneck.
Every task carries who touched it, when it changed, why it failed and what it produced. Reviewing a week of agent work is reading a record — not scrolling back through terminal output that's already gone.