One employee. Real work. Trust earned over time.
Speak or type the brief, hand it real files, connect the work it needs, and start cautiously. Review the proof, correct the same employee, and decide whether Require approval stays on for one Action.
From first brief to useful work
The employee you train is the employee that does the job. One Chat carries the brief, real work, and corrections.
Speak, type, or attach the real work
Press the mic for editable text with the recording kept beside it, or attach a photo, PDF, Word (.docx) file, text file, or CSV. The employee turns what you send into usable text for the job.
Connect the work it needs
Add only the Connectors and data that job needs. Each Connector shows what it can and cannot do before you connect it.
Give it a real job
Start with realistic work. Actions with Require approval on wait in Review; work outside the trusted surface stops.
04 · Accepted work keeps one record
Once Vavio says “on it,” the job has a record.
Vavio records an accepted job before acknowledging it. Closing the browser or an interrupted runtime does not turn that record into a missing job or an optimistic success.
Accepted
Recorded before “on it”
The accepted source and its run are recorded together before Vavio tells the requester it owns the work.
Interrupted
The same run keeps responsibility
If execution is interrupted before it reaches an ending, the platform can retry the accepted work under the same run where safe, instead of forgetting it or inventing a second success.
Honest ending
“Completed” has to add up — and admits it when it doesn’t
Every ending is measured against what the run promised: record, evidence, usage, and delivery. A run that could not keep part of its own record still ends, and says so on itself rather than hiding the gap. If bounded retries cannot finish the work, the same run ends visibly failed with a receipt instead of disappearing.
Durable follow-through is not guaranteed success.
This is Vavio’s product contract for accepted work, not customer evidence, a customer-outcome guarantee, an uptime SLA, or proof that every Connector and Channel has survived every kind of interruption. The narrower claim is the useful one: accepted work must reach an honest ending instead of vanishing silently.
Review the proof, then let trust compound
Evidence makes the work inspectable; correction changes the same employee and informs whether one approval requirement should change.
Review the proof
See what it checked, why it acted or held, the risk, the run cost, and the exact work waiting for you.
Correct it in the same Chat
Approve the exact held work when it is right, or explain what should change in plain language.
See the Training change
The correction becomes a visible rule or preference in Instructions. Every self-update stays visible in History, learned instructions remain editable or removable, and Undo appears only for a conflict-checked exact reversal. The next similar run shows whether it worked.
Keep related work together
Let the same employee keep the shared context, rules, Connectors, and trust when the next job is closely related.
Decide whether approval stays on
Use the evidence to keep Require approval on or turn it off for that Action. Current safety floors still apply independently.
One employee works with your whole team.
Every active teammate can talk to every employee. Each person gets their own continuous conversation, while the employee keeps one business memory and one set of job rules across the workspace.
Your own conversation
Talk in Vavio Chat or through your own linked Slack or Telegram; the employee knows who is asking.
One business brain
Memory, instructions, and business context stay with the employee across the workspace, not in a copy per person.
Authority stays clear
Owners and admins set standing configuration; teammate changes become requests, and work returns to whoever asked.
You see everything it did.
Each run records what woke the employee, what it checked, why it acted or held, what changed, what it cost, and what it learned. That is the proof you use to correct the work and decide what to trust next.
Train your first AI employee today.
Describe the job in plain language, connect the work it needs, and start cautiously. The same employee learns from your corrections and earns trust through evidence.
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