aiagent.app vs Tenor: Company Workspace or AI Business Infrastructure

Adding ten agents creates a management problem before it creates leverage. Someone must define what each agent does, review exceptions, judge results, and decide whether accountability belongs to an agent, a rule, or the process crossing several agents.

aiagent.app puts agents, squads, Workflows, Autopilots, Board, Inbox, and Company Brain in one company workspace. Tenor describes itself as "the infrastructure for the next generation of businesses built around AI", for domain experts who build and scale AI-native service businesses. Both connect company context, run agent work, keep people in control of exceptions, and learn from what happened.

How do aiagent.app and Tenor compare?

Prices and features were checked against each vendor's own pages on 6 October 2026.

Who does aiagent.app fit?

aiagent.app fits a team that wants a configurable surface for several kinds of agent work. Operators can create specialist agents, group them into squads, attach shared and role-specific context, move stable processes into Workflows, schedule them as Autopilots, and put their outputs into the Board and Inbox.

The company view is broad. Company Brain answers questions from connected records with the sources and freshness attached, and run history records what each workflow or Autopilot did, including runs that failed. Team assignments keep the resulting work connected to people.

The tradeoff is operating-model design. The company needs to decide which agents exist, how squads divide work, what context each can access, which process becomes a Workflow, and how the Board and Inbox should be managed. aiagent.app makes those decisions inspectable, and the decisions stay with you.

Who does Tenor fit?

Tenor fits a domain expert building an AI-native service business, such as a specialist who wants agents to deliver a repeatable service to many clients. The expert defines rubrics, permissions, approvals, and the exceptions that go to a person, and Tenor's harness runs the work end to end.

Its distinctive layer is learning. Tenor records decisions and outcomes as evidence and uses that evidence to update the rubric for the next decision, and it says privacy-safe patterns across deployments strengthen its recommendations.

The cost is commitment. Tenor is infrastructure for building a service business, access is by request, and there is no public pricing.

Should you manage a workspace or build on infrastructure?

aiagent.app gives the operator several organizing objects: squads, Workflows, Company Brain, Board tickets, Inbox items, tables, and project context. A team can choose how much of its operation to model in each one.

Tenor keeps the service, its rubric, and its outcomes central. That model suits someone whose product is the work itself and who wants each delivery to improve the next.

aiagent.app gives a team more freedom to compose its company system. Tenor gives a domain expert a consistent base for a business that improves with every outcome.

How do they measure the work?

aiagent.app measures through what the operator can inspect: run history for each workflow and Autopilot, approvals, and the Board's record of who owns what. Its docs describe run history as a way to verify what happened without relying only on the final output.

Tenor measures through outcomes. Each decision becomes evidence, and the evidence changes the rubric.

A buyer should ask which evidence matters. A record of runs helps operate a broad platform. Outcome-driven rubrics help a service improve. Neither automatically proves that one customer-facing result met its acceptance criteria.

Where does Task Machine fit?

Task Machine keeps the recurring job and shared Task stable as humans, Agents, and Workers change. A Playbook installs the Agents, Workflow, Skills, Documents, and setup values for that outcome. Humans and Agents then share ownership, comments, dependencies, and handoffs.

Verifier checks make acceptance part of the process. A separate verifier judges each result against its requirements, sends failed work back with feedback, and pauses work that still fails as an Inbox decision before the run continues. This keeps the job's standard intact when responsibility moves between people and Agents.

Workspace access includes pooled usage instead of a per-member operating plan. Domain experts building a new service business should look at Tenor, and teams that want aiagent.app's Company Brain, squads, and tables should stay there. You keep 100% of your revenue, Task Machine takes no cut, and it never custodies your accounts.

Should accountability belong to the agent, the rubric, or the job?

• Choose aiagent.app when you want a broad hosted workspace to build agents and squads, compose Workflows, organize Board and Inbox work, share company context, and review run history.
• Choose Tenor when you are a domain expert building an AI-native service business and want infrastructure that learns from every recorded outcome.
• Choose Task Machine when recurring jobs should begin as Playbooks, humans and Agents need shared accountable Tasks, and verifiers should gate production Workflows.

Read Task Machine vs Tenor and Task Machine vs aiagent.app for the direct comparisons.