The AI agent task manager
An AI agent task manager is a to-do list your AI agents work themselves: one ranked queue that Claude Code, Cursor or ChatGPT pulls from, completes, and reports back to — so you decide what matters and agents do the work.
Open the board — free →What is an AI agent task manager?
Every task manager you've used so far was built for a human to look at. An AI agent task manager is built for an agent to work. That changes four things:
- One unambiguous next task. An agent asking "what should I do?" gets exactly one answer — the top unblocked item in the queue — not a board of columns to interpret.
- Context travels with the task. Each task is handed out together with its project's working directory, commands and instructions, so the agent never starts blind.
- Results are required. Finishing a task means reporting a short result — your audit trail when agents run unattended.
- Blockers come back to you. When a task needs a credential, a payment or a judgment call, the agent flags it for a human and moves on to the next item instead of stalling.
TaskPrio keeps one ranked queue across all your projects. Lower number = do first. You drag tasks into the order you want; every connected agent works that order top to bottom. Under the hood this happens over the Model Context Protocol — the MCP task manager page covers that layer in technical detail.
Why does one ranked queue beat a project-management tool for agents?
Notion, Jira, Linear and Trello are excellent at what they were designed for: humans coordinating with humans. Their model is many parallel signals — priority labels, sprint columns, assignees, due dates — that a person scans and mentally merges into "what I'll do next".
An agent can't do that merge for you, because the merge is the decision. Three "P0" labels and four "Today" columns give an autonomous agent no single answer to "what's next" — so you end up re-prompting it task by task, which is just a to-do list with extra steps. A single ranked queue removes the ambiguity:
- There is always exactly one next task, for you and for every agent on the board.
- Tasks are leased when handed out, so several agents can drain the same queue in parallel without two of them grabbing the same item.
- Reprioritizing is one drag, not a round of re-prompting every agent session.
See how this compares to the tools you already use on the comparison page.
How do I give my AI agent a task list?
- Capture. Brain-dump your tasks onto the board — or let your agent create them as it discovers work. Rank them once.
- Connect. Claude Code and Cursor connect with a one-line install (setup guides: Claude Code · Cursor); ChatGPT and any other LLM can use the REST API.
- Run. Tell the agent to work the queue. It pulls the top task, does the work, reports a result, and repeats until the queue is empty — flagging anything that needs you.
curl -fsSL https://taskprio.com/install.sh | bashFrequently asked questions
What is an AI agent task manager?
A to-do list your AI agents work themselves: one ranked queue that hands an agent the next task with context, requires a reported result, and routes blockers back to you. TaskPrio is built around exactly this loop.
Can AI agents use Notion, Jira or Linear as a task manager?
They can read them, but those tools were designed for humans scanning boards with many parallel priorities. An agent needs one unambiguous "what's next" with context attached and a way to report back — that's what a single ranked queue provides.
How do AI agents pick which task to do first?
They don't — the queue does. The agent receives the top unblocked task in your ranked order, leased so no other agent grabs it. You steer agents by reordering the queue, not by re-prompting them.
Do I need to know MCP to use this?
No. Claude Code and Cursor connect with a one-line install; other LLMs use the REST API. If you want the protocol-level detail, read the MCP task manager page.