Switch from Tenor to Task Machine

Tenor and Task Machine both support persistent agents doing recurring work with memory, scoped access, guardrails, and human oversight. The move is less about replacing one agent runtime with another and more about changing the operating model. Tenor organizes work around AI workers with roles, managers, responsibilities, and performance measures. Task Machine organizes humans and agents around shared work that moves through chat, inbox, and tasks.

Why do people switch from Tenor?

• The process needs to outlive the worker. Tenor forms a worker around a responsibility and lets it build a configurable workflow. Task Machine makes the workflow graph the stable object, so branches, human questions, approvals, verifiers, and handoffs stay explicit even when the people or agents involved change.
• Judgment is spread across too many workers. Tenor gives each worker review and intervention loops. Task Machine routes approvals, questions, failed checks, proposals, and exceptions from every operation into one inbox with the decision context and available actions attached.
• Humans need to participate in the same work system. Tenor places AI workers beneath the employees who manage them. Task Machine models humans and agents as first-class participants on the same projects, goals, tasks, comments, and workflow runs.
• Control needs to be visible before work continues. Tenor supports guardrails, evidence, and approval boundaries. Task Machine encodes those boundaries as explicit human-question, approval, and verifier nodes with step-level run history attached to the task.

What maps to what?

What do you give up?

Tenor has a clearer workforce-management layer. It is designed to show leaders where AI creates capacity, how often humans intervene, what each worker costs, and whether that spend connects to business outcomes. Task Machine tracks work, run state, usage, and cost, but it does not replace Tenor's executive AI-workforce scorecard. Tenor also gives each worker its own identity and computer as part of a managed operating model. Task Machine asks you to connect workers and the accounts your team already owns.

If the job is rolling out managed AI capacity across an existing organization and measuring employee leverage, Tenor is the stronger fit. Switch when the recurring process, shared human participation, and a single decision queue matter more than managing a roster of AI workers.

How does the switch work?

1. Inventory each Tenor worker's standing responsibilities, tools, access boundaries, review points, schedules, and proof of completion.
2. Create the corresponding agents and human team members in Task Machine, then connect workers and accounts the team already owns.
3. Convert each responsibility into a workflow graph. Preserve the ordinary path, add branch conditions for exceptions, and place human-question, approval, and verifier nodes before consequential actions.
4. Import durable operating context into agent memory or the shared knowledge library, then represent active work as tasks rather than copying historical worker activity.
5. Start with low autonomy, run the workflows on their normal schedules, and resolve the first review cycles from the inbox before expanding authority.

Common questions

Can Task Machine keep a recurring responsibility running?

Yes. A responsibility becomes a scheduled or triggered workflow with an owner, explicit steps, budgets, and gates. The difference is that the workflow remains visible as shared work rather than belonging only to one managed AI worker.

What happens to corrections a Tenor worker learned?

Bring durable rules, examples, and operating context into agent memory or shared workspace documents. Put critical constraints into the workflow itself as branch conditions, approval nodes, or verifiers so they do not depend on one worker remembering them.

Does Task Machine replace Tenor's ROI dashboard?

No. Task Machine exposes run history, task state, usage, and cost, but Tenor's public product is more focused on connecting worker KPIs, human intervention, and AI spend to business outcomes. Keep your existing reporting if that executive workforce view is required.