AI workflow automation
Say what you want. Watch it actually happen.
Automation platforms are good at executing a graph and bad at knowing which graph to execute. Assistants are good at understanding what you meant and bad at doing anything about it. Vyra's job is the seam between the two.
You state an outcome out loud; Vyra picks the workflow, fills in what it knows from persistent memory, runs it, and tells you what happened — or stops and asks first, if the step can't be undone.
What it can reach
n8n workflows
Existing n8n workflows become things Vyra can call. You keep the visual builder, the credentials and the audit trail you already have; Vyra supplies the intent layer on top — which workflow to run, with what inputs, and when it's worth running at all.
A native automation workspace
For automations that don't warrant a separate tool, there's a built-in workspace: triggers, conditions and actions defined in one place and executed by the same runtime that handles everything else.
An autonomous web agent
Plenty of real work has no API — a form to fill, a portal to check, a report to pull. A web agent drives the browser directly so those steps sit inside an automation instead of interrupting it.
Smart-home and device control
The same automation graph reaches physical devices, so a workflow can end in the world rather than in a notification.
Messaging that goes out on its own
Automations can deliver through WhatsApp, Telegram and Discord — useful when the output belongs in a thread with other people rather than in an app you have to remember to open.
Voice as the trigger
Wake word, speaker identification and barge-in mean you start an automation by saying what you want, from across the room, without breaking whatever you were doing.
The n8n integration and the voice trigger path are covered end to end in the workflow automation deep dive.
How this differs from a rules engine
It decides when, not just how
A classic automation platform is reactive: something fires, the graph runs. Vyra's goal engine also decides that a workflow is worth running — background execution checks in roughly every 15 minutes and advances what it can against goals you stated once.
It carries context between runs
Each run isn't isolated. Persistent memory means run two knows what run one found, and knows the people and projects the workflow touches, without you threading state through manually.
It stops before it does something irreversible
A metacognition layer flags consequential or irreversible actions and holds them for confirmation. Autonomy with a checkpoint — the reason you can leave it running without watching it.
It keeps working when the network doesn't
A 5-tier model router falls back to a local Ollama tier when connectivity drops, so the reasoning half of an automation doesn't go dark the moment the WiFi does.
The scheduling half of that is the autonomous goal engine; the routing half is the 5-tier model router. Together they're what an agentic OS means in practice.
Common questions
What is AI workflow automation?
Conventional workflow automation executes a graph you defined in advance: a trigger fires, steps run in order. AI workflow automation adds a reasoning layer that interprets intent, chooses which workflow applies, fills in parameters from context, and handles steps that were never explicitly specified. The automation platform still does the executing — the model decides what to execute and when.
Does Vyra replace n8n or Zapier?
It sits on top rather than replacing them. Vyra integrates with n8n so workflows you have already built keep running in the tool you built them in, with the credentials and version history that live there. What Vyra adds is the intent and scheduling layer above those workflows, plus a native workspace for smaller automations that don't justify a separate tool.
Can I trigger automations by voice?
Yes — that's the primary interface. Real-time voice with wake-word detection, speaker identification and barge-in lets you start a workflow by saying what you want, without opening an app or switching windows.
What stops it from doing something I didn't want?
A metacognition layer performs self-critique and irreversible-action detection: anything consequential — sending on your behalf, deleting, spending, making an external commitment — is held for explicit confirmation rather than executed autonomously. Reversible steps proceed on their own.
Does automation still work offline?
Partly, by design. Steps that need an external service obviously need the network, but the reasoning tier falls back to a local Ollama model when connectivity drops, so triage, drafting and local actions continue instead of the whole system stalling.
Vyra is in closed alpha, with a Founders Beta ahead of public launch in 2026.