NewAgents now operate applications on a Mac

Autonomous agents. Human command.

DeepEngine is the agentic workspace where AI agents execute real work across browsers, business systems and infrastructure. Every action is governed by your policies, approved by your people where it counts, and recorded in a tamper-evident audit trail.

Interactive product demo with sample data
  • Hosted in Switzerland
  • ISO 27001 certified
  • Sandboxed execution per thread
  • Policy engine with deny and approval rules
  • Human-in-the-loop on web, mobile and voice
  • Signed, single-use execution permits
  • Hash-chained audit trail
  • Native MCP and OpenAPI tooling
  • Runs on Claude and Codex
  • Workers for Kubernetes, Linux and macOS
Capabilities

One agent platform for every surface your work runs on

Browser automation

Every agent drives a browser of its own. It authenticates with credentials from the Vault, and you can watch the session live and take control at any moment.

Voice interfaceBeta

Talk to an agent from any room, on the web or on mobile. Long answers are condensed to a spoken summary while the full output stays in the thread.

Knowledge retrieval and memory

Agents run hybrid search across your docs and propose changes for human review. Every room keeps a persistent memory of its own.

Tool orchestration

Connect Linear, Notion, GitHub and more in one step, or bring any MCP server or OpenAPI specification. Agents discover the right tool through search.

Infrastructure and desktop execution

A lightweight worker connects Kubernetes clusters, Linux servers and Macs. On a Mac set up for it, agents operate native applications.

Event-driven automation

Trigger agents from messages, reactions, schedules and incoming events. Scheduled check-ins review open work and speak up only when someone needs to know.

DeepEngine RuntimeComing soon

Managed Linux and macOS runtimes for your agents

Work that needs a machine will no longer need one of yours. DeepEngine Runtime gives an agent a Linux or macOS environment that we operate: a place to run commands, build, and use desktop applications, under the same policies and approvals as everything else.

Linux runtimes
For the commands, scripts and services an agent has to run.
macOS runtimes
For work that needs a Mac and its native applications.
Governed like the rest
Policy, approvals and the audit trail apply as they do on your own systems.
Nothing to operate
We run the machines. You decide which agents may use them.
DeepEngine RuntimeComing soon
  • Linux runtimefor NightwatchRunning
  • macOS runtimefor DigestRunning
  • Linux runtimefor ScoutStarting
  • Commands
  • Files
  • Builds
  • Desktop applications
Workers on your own systems KubernetesLinuxmacOS

Governed autonomy

  1. Agents propose.

    An agent reads, correlates and works out the next action.

  2. Policy decides.

    Your restrictions allow the action, hold it for approval, or deny it outright.

  3. People approve.

    A sensitive action waits for a human decision: on the web, on mobile, or by voice.

  4. Execution is proven.

    The click, the call or the command executes once, and the audit trail keeps the evidence.

click “Confirm delivery date”portal.supplier.example
  1. Proposed Scout, in its room
  2. PolicyNeeds approval Bot browser: buttons named *confirm*
  3. Approved Lea Brunner, 11:24
  4. Executed Once, in Scout’s browser Audit traila41f…9c2e…77b0…

Built for teams that answer for what their AI does

Policy engine

Deny rules and approval rules for commands, file changes, browser clicks and calls to connected services.

Human-in-the-loop

Approvals reach the people authorized to decide: on the web, in the mobile app and on a call.

Zero-trust execution

Workers hold no model and no model credentials. They connect outbound only and accept nothing but signed, short-lived permits.

Tamper-evident audit trail

Every run, approval and action lands in an append-only log chained by hashes, and a check verifies the chain.

Agent builder

Provision an agent in minutes

Give it a name, a purpose and its instructions. Then define what it can reach, how much autonomy it has and who may use it. Each agent gets a dedicated room and a memory of its own.

Example agent
Autonomy level
Access
How agents work
ScoutAgent

Researches and completes work on websites.

InstructionsUse the supplier portal for anything about orders. Report what you found before you change something, and never pay or cancel without approval.

Connected to
Dedicated browserVault credentials for the portal
AtlasAgent

Answers from the team’s docs and keeps them current.

InstructionsAnswer from the docs and cite the pages you used. When a page is wrong or something is missing, propose the change instead of working around it.

Connected to
DocsNotion
FrontdeskAgent

Triages new tickets, finds the likely cause and drafts the reply.

InstructionsFor every ticket assigned to you: read the history, isolate the cause, and leave an internal note with a draft reply. Never send a public reply.

Connected to
ZendeskDocs
NightwatchAgent

Investigates monitoring alerts and remediates what policy allows.

InstructionsWhen an alert arrives, establish what changed before you touch anything. Restart only stateless services yourself; request approval for everything else.

Connected to
Worker prod-clusterWorker web-01Alerts
DigestAgent

Compiles last week’s decisions and open items every Monday.

InstructionsOn Mondays, report what was decided, what is still open and who is waiting for whom. Ten lines at most, and no praise.

Connected to
LinearGitHubA schedule
Autonomy
Asks before changing files or running commands.Changes files freely. Asks before running commands and any other change.Goes ahead unless your rules block it.
Access
Every member of the organization.Only the people you pick. Its room is private.
Integrations

Orchestrated across your stack

Agents act through their browser, through connected services and through workers on your own systems. Read-only tools run immediately. Everything else requires a human decision until you set the policy.

Agents

  • Scout
  • Atlas
  • Frontdesk
  • Nightwatch
  • Digest

Systems

  • GitHub
  • Linear
  • Notion
  • Zendesk
  • Sentry
  • Kubernetes
  • Linux
  • macOS

Also in the catalogue: Asana, Intercom, Stripe, NetBox, 1Password and Alertmanager. Any other service connects through an MCP server or an OpenAPI specification.

Delegation

Delegate the operational backlog

Requests, look-ups, checks and recurring work. Agents pick them up in the channel where your team already works.

  • Has the supplier confirmed our order?
  • Draft a reply to this ticket.
  • Where is the new starter checklist?
  • Why do these pods keep restarting?
  • Sum up this thread for me.
  • Who is on call tonight?
  • What did we decide about the release?
  • Look again in ten minutes.
  • Open an issue for this in Linear.
  • Is the certificate about to expire?
  • Add that to the docs.
  • Which tickets are still waiting?
Always on

Twenty-four hours of autonomous operations

  1. Automation

    The Monday summary arrives

    A schedule starts Digest. It reports what was decided last week and what is still open.

  2. Check-in

    DeepEngine reviews open work

    A question in #purchasing has waited since Friday, so it says so. Otherwise it stays quiet.

  3. Knowledge

    Jonas asks about a new starter

    Atlas answers from two pages and proposes the missing VPN step for review.

  4. Approval

    A click waits for Lea

    Scout wants to confirm a delivery date in the supplier portal. She approves it on her phone.

  5. Ticket

    A customer gets invoices twice

    Frontdesk reads the ticket, isolates the likely cause and leaves a draft reply for the team.

  6. Voice

    Sarah calls from the train

    She asks where the release stands and hears the summary. The full answer waits in the thread.

  7. Monitoring

    db-01 fails its check

    The person on call gets an alarm in the app, and a phone call 45 seconds later.

  8. On call

    Nightwatch has already investigated

    The alert opened a thread in its room. It holds what the agent found on the server.

Compliance

Hosted in Switzerland, inside a high-security bunker

DeepEngine runs in a high-security bunker data center in Switzerland. Both are ISO 27001 certified: the data center, and we ourselves. The place your agents work from is as protected as the rules they work under.

  • Hosted in SwitzerlandThe platform and the data it stores are hosted in Switzerland.
  • High-security bunker data centerThe servers stand inside a bunker, behind rock and controlled access.
  • ISO 27001 certifiedTwice over: our own information security management system is certified, and so is the data center.
Security architecture

Questions teams ask first

Where is DeepEngine hosted?

In Switzerland, in a high-security bunker data center. Both the data center and we ourselves are ISO 27001 certified. Requests to the AI model are the exception: they go to the model provider configured for your organization.

What is an agent in DeepEngine?

A persona of DeepEngine with a name, a purpose, its own instructions, a dedicated room and its own memory. In the product it is called a bot. You connect what it should work with and decide who may use it. DeepEngine itself is the agent every workspace starts with.

What can an agent act on?

Its own browser, your docs, the services you connect, and workers on your Kubernetes clusters, Linux servers and Macs. Without any of them it still answers from docs and memory.

How much autonomy does an agent have?

As much as you grant. Each agent has a level: ask first, accept edits, or auto. Your restrictions apply on top and either deny an action or require an approval. Full access is not on offer.

What has to be deployed?

Nothing to start. A worker is only needed on systems an agent should operate. It ships as a Helm chart for Kubernetes, a systemd service for Linux and a launchd service for macOS, and connects with a one-time install command.

Is there a voice interface?

Yes, in beta. Start a call in any room on the web or in the mobile app. What you say is posted as your message, the agent answers aloud, and you can give an approval by voice.

Which models does it run on?

Claude or Codex. The default is set per organization, and each agent can run on the other one. Requests to the model go to its provider, Anthropic or OpenAI, with the key configured for your organization.

Where does an agent run?

In a sandbox that DeepEngine operates: one isolated container per thread. A worker on your system holds no model, no provider keys and no conversation, and it only connects outbound.

Can agents run on machines you operate?

Soon. DeepEngine Runtime will provide Linux and macOS runtimes that we operate for your agents. Today, agents act through their browser, through connected services and through workers on your own systems.

Put your first agent into production

Book a demo and follow an agent from the first request to completed, audited work.