Task Machine vs aiagent.app

Choose aiagent.app if you want to design a company-wide agent system from visual building blocks. Choose Task Machine if you want to start with a recurring job, install its operating pattern, and have people and Agents carry it together through shared Tasks.

Both products combine agents, Workflows, schedules, approvals, knowledge, an Inbox, work tracking, run history, and usage reporting. The practical difference is where you spend your setup time and how your team reviews the work each day.

aiagent.app or Task Machine at a glance

If you care most about	Choose aiagent.app	Choose Task Machine
Getting started	Build an agent, attach its knowledge and tools, then place it in a Workflow or Squad	Choose a job-first Playbook and install its Agents, Workflow, Skills, Documents, and setup values
Daily work	Operate agents through Chat, Board, Inbox, Autopilots, and company-level Performance	Direct work in Chat, resolve judgment calls in Inbox, and steer the details in shared Tasks
Team participation	Assign Board work, review Inbox items, and approve proposed actions	Give humans and Agents the same Task ownership, comments, assignments, and handoffs
Checking output	Use guardrails, scorecards, approvals, traces, Audit Log, and Performance reporting	Put an independent verifier node inside the production Workflow and stop failed work before it continues
Execution	Use a hosted product with more than 1,000 promoted application connections	Use Managed Cloud or an optional Local Worker for supported coding-agent tools
Usage model	Choose from tiered plans, a per-member Teams offer, provider-key options, or managed implementation	Buy Workspace access with included usage pooled across human and Agent members, then pay for additional usage

Who should choose aiagent.app?

Choose aiagent.app when your team wants to assemble and inspect the agent system itself.

Its agent builder exposes the role, model, knowledge, memory, skills, connections, guardrails, and scorecards. Its Workflow canvas adds tools, branches, loops, waits, approvals, typed handoffs, and durable step state. Squads coordinate specialist agents, Company Brain provides shared context, and scheduled Autopilots turn a tested runbook into recurring work.

aiagent.app also gives operators a company-level Performance view covering activity, completion, reliability, usage, and cost, with paths into runs and Audit Log events. Task Machine does not offer an equivalent aggregate dashboard today.

This amount of choice creates setup work. Your team must decide which agents and squads should exist, what each can access, when a process needs a Workflow, and how Board and Inbox should be managed. aiagent.app offers templates and managed implementation if you do not want to do all of that configuration yourself.

Who should choose Task Machine?

Choose Task Machine when you can name the recurring job and want the starting process installed before you configure every part.

A Playbook packages the Agents, Workflow, Skills, Documents, and setup questions for an outcome such as client reporting, payment recovery, outreach, or support triage. You preview the resources before installation, answer the values that vary for your business, and keep the installed resources editable.

Humans and Agents then use the same work system. Either can own a Task, comment, participate in a Project, or hand responsibility to the other. Chat is where you discuss direction and create work. Inbox contains approvals, questions, proposals, failed verifiers, and exceptions with the context and actions needed to resolve them. Tasks retain the working conversation, dependencies, and run state.

Task Machine asks you to work from an explicit job and acceptance path. It does not reproduce aiagent.app's visual Squad builder, Company Brain, table surface, or company-level Performance view.

How would each product run a weekly client report?

In aiagent.app, you might create a research Agent and a writing Agent, connect the relevant company knowledge and applications, and coordinate them in a Squad or visual Workflow. An Autopilot runs the process each week. The draft can become Board work or an Inbox review, and Performance shows how the Agents and runs behave over time.

In Task Machine, you would install the closest reporting Playbook or build one Workflow around the job. The Workflow assigns research, drafting, review, and delivery Tasks to the appropriate humans and Agents. A verifier checks the stated acceptance criteria before the run continues. If the report fails that check, Inbox presents the evidence and the available resolution actions. You schedule the Workflow after one representative run passes.

aiagent.app gives you more freedom to design the agent company around the report. Task Machine gives the report a reusable operating pattern with shared ownership and an explicit acceptance gate.

How do approvals and failed checks differ?

aiagent.app can durably pause a Workflow or pending tool action around a concrete proposal. An authorized person sees the proposed action and its arguments, approves or rejects it, and leaves that decision attached to the run. This is a real runtime control, not a prompt asking the model to wait.

Task Machine uses human-question and approval nodes for those decisions. It also provides a separate verifier node. The Agent that produces the work does not decide whether its own result meets the acceptance criteria. A failed verification stops the run and creates an Inbox item with the Task, evidence, and resolution actions attached.

Choose aiagent.app if scorecards, traces, approvals, and company-level performance trends answer your control questions. Choose Task Machine if the production process itself needs a named pass-or-stop gate before work continues.

Where does the work run?

aiagent.app runs as a hosted product connected to approved tools. Its public site promotes more than 1,000 application connections, and paid plans can use customer-provided model keys. The reviewed public material does not show a customer-operated local execution option.

Task Machine uses Managed Cloud for ordinary execution. Technical teams can also install a Local Worker for supported coding-agent tools that need local repositories, files, browsers, or CLIs. The Local Worker gives the team control over that execution environment, but the team must install and operate it.

aiagent.app requires less execution infrastructure from the customer. Task Machine offers an additional boundary for supported coding work that depends on a local development environment.

How should you compare pricing?

aiagent.app publishes tiered self-serve plans, a per-member Teams offer, paid-plan provider keys, and a separate managed-build service. Its managed-service pages have displayed conflicting prices and delivery promises, so confirm the current implementation scope and commercial terms directly with aiagent.app.

Task Machine charges for Workspace access and pools recurring included usage across the Workspace. Additional model and execution usage is metered. External provider charges remain separate when a Workflow acts through a third-party service.

Compare the number of people, expected runs, model costs, external service fees, and implementation work. A per-member plan maps cost to team size. Pooled Workspace usage maps cost to the work performed by the combined human and Agent team.

You keep 100% of your revenue, Task Machine takes no cut, and Task Machine never custodies your accounts.

Which product should you use?

Choose aiagent.app if you want:

• a visual system for building Agents, Squads, and Workflows
• Company Brain and broad hosted application connections
• company-level Performance and Audit Log views
• provider-key options or managed implementation
• a fully hosted execution boundary

Choose Task Machine if you want:

• a job-first Playbook that installs an editable starting process
• humans and Agents sharing the same accountable Tasks
• every judgment call resolvable from one Inbox
• an independent verifier gate inside the production Workflow
• pooled Workspace usage or optional local execution for supported coding work

If your current aiagent.app system already runs reliably and its Board, Inbox, and Performance views match how your team operates, switching may add work without enough benefit. If the recurring job, handoffs, and acceptance gate need to remain stable as people and Agents change, Task Machine is the closer fit.

What should you know before choosing?

Does Task Machine import aiagent.app configuration or history?

No. The aiagent.app migration guide describes a manual, job-by-job rebuild. Agents, Squads, Company Brain data, Workflow graphs, Board tickets, run history, Audit Log events, and Performance data do not transfer automatically.

Does aiagent.app support human approvals?

Yes. aiagent.app can pause Workflows and pending tool actions around a concrete proposal, then preserve the approval or rejection with the run.

Does Task Machine replace aiagent.app's Performance view?

No. Task Machine records Workflow runs, Task history, verifier decisions, and usage. aiagent.app presents a broader company-level aggregate Performance surface.

Can Task Machine run everything on a Local Worker?

No. Local Workers are optional and support specific coding-agent tools. Managed Cloud remains the default execution path for other work.