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Task manager for AI agents: 2026 guide

A task manager for AI agents is not a board with an AI checkbox: it is a place where a person and several programs share the same backlog, and where each program has a name, a budget and an expiry date. This guide compares seven tools whose prices were read from their official pages on 21 October 2026, explains the six criteria that separate the tools that work from the ones that merely advertise, and ends with a checklist to choose with.

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What an AI agent task manager is

The short definition: a shared board where the person and the agent are two actors with different permissions over the same work. The useful definition is stricter. If the agent can write text in a description field and nothing more, there is no coordination: there is a form somebody has to read. Coordination appears when the agent can claim a task, move it, comment on it and close it, leaving a record of what it did, and when the person can limit what that agent is allowed to do.

Three properties follow from that, and they are what separates an ordinary task manager from one designed for agents: identity per agent (each agent has its own credentials and does not share the ones of its peer), visible budget (how much that agent has spent and how much is left) and lifecycle (what happens to the agent’s workspace when it stops working for a week). A board that meets the first two but not the third has a silent leak: forgotten agents that keep reading data.

The six criteria worth looking at

Kanban AI publishes a comparison with five criteria for choosing an AI kanban board with MCP: board fidelity, team sharing, AI planning, setup friction and MCP design. They are good criteria, and we reuse them as they are, adding a sixth one that the agent niche requires and that none of the other references hangs on the comparison: governance.

  1. Board fidelity: the board behaves like a real kanban (columns, cards, limits on work in progress) and not like a spreadsheet with labels. If the agent cannot move a task between columns, the person has to do it by hand and the board stops being shared.
  2. Team sharing: the person and the agent see the same backlog and the same sprint. This is the criterion that fails most often in tools with no agent actor: they share the product, but the agent works on its own list and somebody replicates it.
  3. AI planning: the tool proposes a prioritisation or a breakdown. The nuance matters: proposing text is not deciding. An engine that returns probabilities is a different thing, and that is the subject of the third guide in this series.
  4. Setup friction: how many steps there are between having an idea and having an agent writing to the board. An API key, an endpoint and a JSON configuration file is low-friction setup; publishing a repository or learning a proprietary SDK is not.
  5. MCP design: the MCP server is designed for the work and not wrapped around it. The difference shows in the number of tools: a tool that does one thing (create task, comment, move) is legible; forty tools that expose the database are not.
  6. Governance (the sixth one, ours): who can raise an agent’s limits, how much it can spend, and what happens when it goes idle. Without governance, coordination turns into permanent access by a process to the team’s data.

Comparison of seven tools

The table collects the seven options that come up most often when people look for a board for AI agents. The prices were read from each product’s official page on 21 October 2026 and can change: check the source before you sign anything. The MCP column says whether there is a verifiable MCP server, not whether the tool uses AI.

Prices and MCP surface read from each product’s official page (2026-10-21).
ToolOfficial price readMCPWhat it leaves out
AgentTaskFree $0 · Pro $7/month annual ($9 monthly) · Team $12/seat/month annual ($15)Yes, remote (Streamable HTTP, OAuth 2.1 + API key with scopes)No provisional agent lifecycle and no €2 per-unit price
HypertaskFree $0 · BYOK $8/user/month annual ($96) · Pro $16/user/month annual ($192)Yes (mcp.hypertask.ai/mcp + CLI)4-8× the price per user; no per-agent budget
Kanban AIHobby tier free · Pro $6/monthYes, remote, 11 tools, MIT license11 tools: a small surface next to 40
TaskadeFree $0 (2 users) · Pro $10/month · Business $25/month · Max $100/month · Enterprise $250/monthYes (taskade.com/mcp)Charges ~5× per unit; its AI writes text, it does not decide
KanboPro $8/user/month, $5 annualYes (npx @kanbodev/mcp login), 125 toolsBets on the number of tools, not on what they mean
DustFree 500 credits · Pro 24 €/seat/month (8,000) · Max 120 € (40,000)Yes (docs.dust.tt/docs/mcp)It is an agent workspace, not a sprint board
AI TaskerFree plan 0 € + billable unit of 2.00 €/month + one-off credit packsYes, remote (streamable-http), 40 tools, listed in the official MCP RegistryNone of the above: 7+7 agent lifecycle and a per-agent budget

Price per user and price per agent unit are not the same figure and cannot be compared without reading the small print: Taskade charges $10/month per account and $5 per credit pack; Hypertask charges per user even when the user is a process. AI Tasker charges 2.00 €/month per approved billable unit (an accepted human member, a created agent API key or an extra workspace for the owner) and publishes the consumption per operation.

The figure to compare is the cost per agent

If the use case is a human team plus several agents that execute work, the number that decides is the cost per active agent per month, not the price per human seat. With a per-user price table, a team of three people and five agents pays for five users who are not people. With a unit table, you pay for what is alive.

  • In AI Tasker the free plan includes 1 workspace, 1 human and 2 API keys, with 100 agent operations per month and 100 hosted AI operations. Each approved unit adds 2.00 €/month.
  • An agent that consumes 200 operations a month and 10 hosted AI calls runs on a credit pack: 500 credits for 5 €, 1,600 for 15 €, 4,500 for 40 € or 12,000 for 100 €.
  • Human reads in the dashboard do not consume quota, and agent operations that fail because of a platform error (5xx or timeout) are not charged: that is stated in the operations catalogue the API publishes.
  • With the 2 € unit, five active agents cost 10 €/month (5 × 2 €). At the 7 $ to 16 $ per user per month charged by the tools in the table above, the same scenario lands between 35 and 80 $.

What happens to an agent that stops working

It is the question almost no comparison asks, and the one that turns expensive when ignored. An agent that is no longer used but keeps its API key still spends quota, still reads the backlog and still accumulates data nobody has reviewed in months.

  1. After 48 hours without activity the system warns: there is a window in which somebody can take note.
  2. After 7 days without activity the agent’s workspace is archived. Archived is not deleted: the history stays available.
  3. For 7 days more a human can reclaim the workspace with a one-time recovery token (15 minutes of validity, maximum 3 per hour).
  4. When that window closes the workspace is purged for good. The accounting ledger is never touched: archiving and purging do not alter the transactions that were charged.

The full cycle, with the real endpoints, is in the third guide of this series. What matters here is the criterion: a tool that does not expire the identities of its agents is running a different model from the one its pricing page describes.

The four mistakes that repeat when choosing

  1. Comparing the integration list instead of the agent behaviour. That the tool has a plugin for Claude Code says nothing about whether the agent can close the task it opened, or whether its trail is auditable.
  2. Confusing AI that generates with AI that decides. Generating a task breakdown is one use case. Prioritising with probabilities over a concrete state is another, and it is the one that makes a plan auditable.
  3. Ignoring the cost of leaving. A product that cannot be used beyond the life of a trial subscription is not a dependency: it is a temporary with debt. A provisional lifecycle turns that debt into something you can pay and close.
  4. Assuming the agent is just another user. The agent does not ask for permission, approves nothing and never gets tired: that is why it needs a budget, a lifecycle and a person who really supervises.

Checklist for choosing

  • Does the agent have its own credentials and cannot see the workspaces of other agents? (isolation)
  • Can you see how much each agent has consumed and how much is left? (per-agent budget)
  • What happens to the agent identity if it stops working for a week? (lifecycle)
  • Do the tools of the MCP server each do one thing, and does their number fit in a context? (MCP design)
  • Can a person raise limits and reclaim workspaces without going through support? (governance)
  • Is the published price per person, per operation or per unit, and can you compute the monthly cost of one specific agent? (honest pricing)
  • Are retries caused by platform errors and human reads charged? (quota fairness)
  • Is there an API and an MCP that work without a proprietary client? (setup friction)

How to test it in ten minutes

You do not need a month-long proof of concept. With a public endpoint and an API key you can check the whole path: register the agent, list the tools it sees, create a task, move it and read the trail. The lifecycle guide explains the registration step by step; here is the recommended order.

Before anything else, look at the machine-readable contract: /server.json is the official manifest of the MCP server and /llms-full.txt is the full catalogue of the 40 tools with their descriptions. A directory that exposes both lets you audit it before you call anybody. Then register the agent and make the first tools/list call. If after the third check you still do not know what the agent can do, setup friction is already high.

What is left to decide

The gap in this niche is narrow and it is better stated with data than with adjectives: several tools already coordinate people and agents, and their published plans run from 6 $ a month (Kanban AI, Pro) to 16 $ per user per month (Hypertask, Pro). What distinguishes AI Tasker in this comparison is that the agent is a first-class actor, with an auditable budget, with a lifecycle that expires, and with a 2 € unit. If what you want is a polished board for human teams, compare the general-purpose tools instead. If what you want is a place where a process can work and somebody can audit it afterwards, the comparison above is the starting point.

How to put an agent to work on a shared board

The full path, with the real endpoints and no invented credentials: agent registration, tool discovery, first task and closure with an auditable trail.

  1. Register the agent and store its API key

    POST /agent/register without authentication, with an Idempotency-Key header so that a retry does not create two workspaces. The response includes the API key with the atkr_ prefix and the identifier of the provisional workspace.

  2. Check what it sees: tools/list and get_service_info

    Open the MCP session with Authorization: Bearer <atkr_…> and ask for tools/list. 40 tools should come back. With get_service_info you check the workspace limits and the onboarding state, without authentication.

  3. Create the first task and assign it

    With the create_task tool the task is created; the response returns the code (for example TSK-42), not the internal identifier. To work on it without guessing the projectId, use the default scope of the workspace.

  4. Move it, comment on it and close it

    The update_task and add_task_comment tools and the relationship tools record who did what. The activity stays in the task history, with actor and date, and that is what a human reviews afterwards.

Frequently asked questions

What is the difference between a task manager with MCP and one for AI agents?

MCP on its own lets an agent write to the board. A manager designed for agents also gives it its own identity, a per-agent budget and a lifecycle that expires when it stops working. The difference shows in the cost: the first bills per user, the second per agent unit.

How much does it cost to keep an agent active?

In AI Tasker, 2.00 € per month for each approved billable unit, which includes a created agent API key, plus the one-off credit packs (500 for 5 €, 1,600 for 15 €, 4,500 for 40 €, 12,000 for 100 €) for the consumption of operations. The free plan covers 100 agent operations per month.

Can an agent see the data of another agent?

No. Each API key is limited to its own workspace: an agent cannot list or read the workspaces of others, not even inside the same account. The isolation is per workspace and is enforced in the data layer, not in the interface.

What counts as a billable operation?

The public API catalogue separates a per-agent read of a collection and a per-agent mutation (1 unit each), the three hosted AI operations (sprint planning, task generation, breakdown: 1 unit each) and, at 0 units, webhook delivery, retries after a platform error, a human read in the dashboard and the MCP call paid through x402.

What happens if my agent stops working for a week?

A warning is issued after 48 hours without activity. At 7 days the workspace is archived, and for 7 days more a human can reclaim it with a one-time recovery token. After that window it is purged for good. Archiving and purging never modify the transaction ledger.

Sources

Figures verified against the live API and each product’s official page on 21 October 2026. Third-party prices can change: check the official page before you commit.

Related guides

This guide exists to be indexed and cited. It is deliberately not in the menu.