aiagent.app vs Hyperagent: Workspace or Living Deliverable
aiagent.app organizes agents and recurring work. Hyperagent gives a cloud agent responsibility for building and maintaining a living deliverable.
Founder, Task Machine
Review has a concrete target when everyone can point to the object that must remain correct. A website, dashboard, or deck provides that object naturally. Support triage, outreach, and recurring reporting spread responsibility across many records, decisions, and handoffs.
aiagent.app gives the company a workspace containing agents, squads, Workflows, Autopilots, Board, Inbox, and Company Brain. Hyperagent asks a cloud agent to build and maintain a living deliverable such as a site, dashboard, document, deck, or video. Both connect existing systems, expose agent activity, and retain context across runs.
How do aiagent.app and Hyperagent compare?
| Dimension | aiagent.app | Hyperagent |
|---|---|---|
| Primary object | Hosted company workspace with agents, workflows, accountable work, context, and review | Living deliverable built and maintained by a cloud agent |
| Typical output | Recurring operations, Board tickets, reports, decisions, and tool actions across departments | Websites, dashboards, documents, decks, videos, and other maintained artifacts |
| Human role | Configure agents and workflows, assign work, review Inbox items, approve actions, and check run history | Brief the desired deliverable, observe searches and decisions, and review the result |
| Process structure | Visual Workflows with branches, loops, waits, approvals, typed handoffs, and step state | Prompt-first agent execution inside an isolated cloud environment |
| Context and learning | Company Brain, knowledge, memory, skills, and connections | New memories and skills the agent generates as it works |
| Coordination | Squads, Board, Inbox, assignments, and scheduled Autopilots | Agents triggered through product channels, schedules, webhooks, Slack, or Telegram |
| Models and systems | Multiple models, hosted connections, and provider keys | Multiple model providers, with each agent in its own computing environment |
| Commercial shape | $49, $149, or $399 a month, plus Teams at $79 per user | No public pricing page, with example artifacts showing their run cost |
Prices and features were checked against each vendor's own pages on 6 October 2026.
Who does aiagent.app fit?
aiagent.app fits teams that need several forms of agent work to coexist. A growth audit, support triage, content workflow, and competitor watch can use different agents and workflows while sharing Company Brain, project context, a Board, and an attention Inbox. Squads let specialists delegate. Scheduled Autopilots turn stable runbooks into recurring operations.
The visual builder keeps role, model, knowledge, memory, skills, connections, and guardrails inspectable. When the process matters, the operator can move agent judgment into a Workflow with required branches, waits, durable approvals, and run history.
The team must still decide how those parts create a coherent operation. aiagent.app provides the workspace and components. Templates and managed implementation can shorten the path, but ongoing ownership spans agents, context, workflows, projects, and review queues.
Who does Hyperagent fit?
Hyperagent fits work whose success is visible in one artifact. A website should reflect current inventory. A dashboard should update as source data changes. A deck or document should incorporate new research. The agent can research, build, and maintain that deliverable while the user watches its searches and decisions.
Prompt-first execution removes several configuration steps between the brief and the output. Agents run in their own cloud environments, connect to systems the customer owns, and can be triggered from schedules, webhooks, Slack, or Telegram. It learns as it goes and generates new memories and skills, so the output can improve over time.
The model maps less directly to work without a single artifact. Support, outreach, reporting, and follow-up can produce many records and decisions across several owners. An operating workspace or explicit process keeps those handoffs visible.
Do you need to manage the system or the result?
aiagent.app lets an operator model who works, what they know, which tools they can use, how a process branches, which action needs approval, where the output lands, and how company activity changes over time. The object under management is the agent system and its company work.
Hyperagent asks for the desired outcome and lets the agent hide more of the route. The user can inspect activity and evaluate the result, while the maintained output remains the center of the product. This removes process modeling when the artifact itself provides a natural acceptance test.
Neither model removes judgment. aiagent.app distributes judgment across configuration, workflow controls, Inbox, and Board. Hyperagent concentrates it around the brief, visible agent activity, and the deliverable review.
How do their checks differ?
aiagent.app uses guardrails, approval steps, and run history. The operator can review a proposed action before execution and open any run to see what happened, including runs that failed.
Hyperagent shows every search, decision, and artifact as it works, and the agent learns from what it builds. That is well matched to an artifact whose quality can be assessed repeatedly. A site, dashboard, or document can be tested and refined around the same durable object.
The public products do not present the same checking model. aiagent.app's controls span company operation and runtime approval. Hyperagent's center is visible work on one artifact and an agent that learns. Buyers should ask whether they need authorization around consequential actions, quality iteration around an artifact, or both.
Where does Task Machine fit?
Task Machine keeps the recurring job and its accountable Task stable as outputs and owners change. A Playbook installs the Agents, Workflow, Skills, Documents, and setup values for that job. Humans and Agents then share ownership, comments, assignments, and handoffs.
Verifier checks on a step judge the result against its requirements, independently from the Agent that produced it. Failed work goes back with feedback, and work that still fails pauses as an Inbox decision with the evidence and resolution actions attached. This fits a process with several outputs better than a review centered on one maintained artifact.
Task Machine requires more process definition than Hyperagent's prompt-first deliverable and lacks aiagent.app's Company Brain and squads. You keep 100% of your revenue, Task Machine takes no cut, and it never custodies your accounts.
Is the durable result an artifact or a process?
- Choose aiagent.app when you want one hosted workspace for configurable agents, squads, workflows, Board work, Inbox review, company context, and run history.
- Choose Hyperagent when the job is a living deliverable and you value prompt-first execution and visible agent activity.
- Choose Task Machine when the recurring process and its handoffs should remain explicit, humans and Agents need shared accountable Tasks, and verifier gates define acceptance.
For the direct product views, read Task Machine vs Hyperagent and Task Machine vs aiagent.app.