What happens when your digital coworker runs on your own model?

Key facts

Agent runtime	Sovereign AI agent in the Plugsky Playground Beta
Model dependency	Runs on Plugsky's own models — no external model calls
Deployment	Plugsky cloud, your VPC, on-prem, air-gapped
API	OpenAI-compatible endpoint
Free plan	2 free AI models (plugsky-micro, plugsky-lite), no card
Trial	14-day full-access trial
Models	30+ models through one API
Product status	Playground Beta (agent runtime)

TL;DR

• Digital coworkers become practical when they run on a model you control.
• Plugsky's Playground Beta gives the agent a goal and returns a finished deliverable.
• Sovereign by default: no external model calls, and data stays in-region.
• Human approval gates and audit logs keep risky actions accountable.
• Start on the free plan with two free models, then scale via the live pricing page.

How it works, step by step

1. List the tasks you would hand to a digital coworker and the data each one touches.
2. Confirm the residency requirement for that data: region, VPC, on-prem, or air-gapped.
3. Create a Plugsky account and try the Playground Beta on a non-critical workflow.
4. Map each task to a model tier and route routine steps to smaller in-house models.
5. Add human approval gates wherever an action moves money or changes production.
6. Log agent actions against identities before expanding scope.
7. Move the workflow to production against your own endpoint and keys, then review cost.

Original data

Try it yourself

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The shift from chatbots to digital coworkers

Manus and Genspark pushed the idea of a digital coworker into the mainstream: an assistant that plans a trip, writes the report, or builds a small app on request. The next question buyers ask is not what it can do, but whose model does the work and where the data goes.

Plugsky's Playground Beta runs a sovereign AI agent that takes a goal, plans it, executes with tools, and returns a finished deliverable — on Plugsky's own models, without external model calls.

Why "whose model?" is the real question

A digital coworker touches your documents, your mailbox, and your internal systems, so the model that reasons over that data becomes part of your security boundary. If those prompts are processed by a third party in another jurisdiction, a productivity tool quietly becomes a data-flow decision.

Running the agent on models you control, in the region you choose, keeps that boundary intact while you still get multi-step automation.

What you can hand it today

In the Playground Beta you can give the agent a goal and watch it plan, run steps, and produce an artifact. Typical uses are research briefs, structured extraction, report drafts, and multi-step operations against sandboxed tools.

Risky actions should sit behind a human approval gate, and every agent action should be bound to an identity with an audit log so the work can be reconstructed later.

A pragmatic adoption path

Start with one non-critical workflow, confirm the residency requirements for its data, and pilot the agent against it. Map each task to a model tier and route routine steps to smaller in-house models to keep costs predictable.

Expand scope only after approval gates and audit logs are in place. The free plan includes two free AI models and a 14-day full-access trial covers the full catalog; see the live pricing page for current plans.

Honest comparison

Capability	Plugsky	Rented digital coworker	Building in-house
Model ownership	Your model on your infrastructure	Vendor's model, vendor's cloud	You host every model
Data path	Stays in your region or perimeter	Routes through the vendor's cloud	You control
External model calls	None required for the agent runtime	Standard	None, but heavy operations
Human approval gates	Built into agent workflows	Varies by vendor	You build them
Time to first result	Start in the Playground Beta	Minutes, with data leaving home	Months of platform work