AI agents that can work
wherever your data lives.
Raigent connects governed AI agents to tools, workflows, and remote computers across labs, company sites, clouds, and on-prem environments — all controlled from one place.
Built for environments where compute doesn't live in one cloud
Frameworks build agents. Clouds host them. Nobody connects the real world.
There are dozens of frameworks for building local agents and plenty of managed clouds for hosting them. But your compute, data, and instruments are distributed across sites, networks, and trust boundaries — and that's exactly where agents struggle to reach.
Compute is fragmented
A GPU box in a lab, a workstation behind a factory firewall, jobs in two clouds. Each is isolated, each needs its own access story, and none of them talk.
Networks fight you
On-prem and lab machines sit behind NAT and firewalls with no inbound access. Today that means VPNs, bastion hosts, and tickets — brittle plumbing that breaks at the worst time.
No single pane of glass
Once agents are running in five places, you lose the thread: who called what, which tools are reachable, what's failing. Security and ops fly blind.
One control plane. Agents, tools, and devices across every environment.
Remote workers and device services poll out to Raigent. The control plane applies tenant-scoped policy, dispatches durable work, and keeps operations visible — no inbound firewall rules required.
Connect the environments you already have
Register agents, tools, and remote computers to a tenant. Each device receives its own identity, queue, and deliberately narrow policy.
They call out, not the reverse
Workers poll Raigent over outbound connections. No open inbound ports, no VPN, and no general-purpose remote shell exposed on the network.
The control plane authorizes every job
Raigent resolves the tenant, target, workflow, and allowed operation before work reaches a remote environment, then tracks the durable execution.
Bring customer environments into the governed mesh.
Connect a lab PC, workstation, on-prem server, private MCP service, or customer compute platform. Raigent agents can use only the capabilities explicitly approved at that edge.
Admin-controlled enrollment
Create, rotate, revoke, and monitor tenant-scoped device credentials from the Raigent console.
Operator-approved capabilities
Local policy controls file roots, MCP tools, compute templates, cloud identities, and network access. The platform cannot supply arbitrary commands or infrastructure settings.
Files and results take the direct route
Large inputs and outputs move through approved object storage. Workflows carry bounded references, so file bytes and compute results do not accumulate in workflow history.
list-remote-devicesfoundlist-remote-directory12 entriesupload-remote-fileverifiedRun each capability in the right place.
Low-latency tools scale as private services, batch workloads run as durable jobs, and customer-scale compute remains in the customer's cloud behind Raigent Edge. Raigent coordinates identity, policy, retries, cancellation, and results across all three.
Private services
Queries, inference, and interactive tools run in private request-driven services that scale quickly with demand.
Durable jobs
Ingestion, training, forecasting, and larger transforms run as managed jobs with durable coordination and observable outcomes.
Customer compute
Approved workloads run on customer infrastructure through Edge while cloud identities, networks, images, and resource policy stay customer-controlled.
Everything you need to run agents across trust boundaries
One control plane for policy, connectivity, and observability — so distributed agents behave like a single, governed system instead of a pile of scripts.
Outbound-only connectivity
Agents and tools reach the control plane with outbound polling, so they run behind NAT and firewalls with zero inbound exposure. Reach machines you could never reach before — without opening them up.
Policy-driven mesh
Declare exactly which agents and tools may communicate, in which direction, for which operations. The control plane is the single point of enforcement — change a rule, the mesh reconfigures.
Durable agent operations
Conversations, schedules, memory, delegation, and multi-step tool use survive restarts. Requests retain their identity and state across service and network boundaries.
Cross-cloud & on-prem
The mesh spans clouds, regions, and your own data centers as one fabric. An agent in AWS can safely use a tool that lives on a machine in your lab — brokered, logged, and governed.
Console, SDK & MCP
Manage agents, skills, tools, workflows, schedules, mesh policy, and devices in one console — then integrate with applications through the SDK or MCP.
Framework-agnostic tools
Bring agents built with any framework and expose any capability as a tool. Raigent doesn't replace how you build — it's the connective tissue that lets those agents work across sites.
Ready-to-use data intelligence
Discover and ingest data, run bounded queries and statistics, generate charts, train AutoML models, and diagnose or forecast time series.
Governed model access
Route model and embedding calls through one controlled gateway with tenant attribution, usage accounting, audit context, and configurable guardrails.
From enrollment to useful work in a few controlled steps
- ✓Create the device in the Raigent console and issue its one-time credential.
- ✓Enroll the service without putting secrets in command history.
- ✓Keep the worker connected in the background while the desktop window is closed.
$ raigent-device enroll --config /etc/raigent/config.json
Device credential: ••••••••••••
✓ credential stored securely
# run as a background service
$ raigent-device start
✓ service started
$ raigent-device status --config /etc/raigent/config.json
device/research/lab-pc-17 · connected
Bring your agents to the work — without opening your network.
Raigent is in early access. Tell us where your agents, tools, and remote computers need to work, and we'll help you connect them safely.