Embedding autonomous AI agents into your operations with no security gaps
We design and run agent environments where the agent does real work on your code and systems, while your credentials, network and history stay out of its reach.
Agents are useful because they act. That is also the risk.
Most agent pilots hand the agent the same access a developer has. When the agent reads a poisoned web page or a malicious file, it can be steered with that access.
Secrets in reach
API keys and tokens sit in the agent's environment, where an injected instruction can read and send them.
Open network
An agent that can call any address can quietly move data out, and nobody sees it until later.
State that lingers
Long-running sessions carry earlier instructions forward, so one bad input can shape every task after it.
Control stays with you. The agent works in a sealed room.
Two layers, one checkpoint between them. Select any part to see what it does and which risk it closes.
Host control plane
MicroVM agent context
Egress proxy
Every outbound request is checked by a policy the agent cannot touch.
The proxy runs outside the virtual machine. It allows only the destinations you approve and adds credentials to requests on the way out, so the agent never holds them.
Closes: data exfiltration and credential theft through prompt injection.
What holds true on every run
These properties come from the architecture, not from asking the model to behave.
- Your credentials never enter the agent's machine.
Keys live in the host control plane and are attached at the proxy. A compromised agent has nothing to leak.
- Every outbound call passes a policy you own.
An allowlist enforced outside the VM decides where traffic can go, and every request is logged for audit.
- Every task starts clean and is thrown away.
Fresh context, a fresh microVM and only the current code. Nothing carries over from one task to the next.
- The agent cannot mark its own homework.
A guard hook protects the tests and checks that judge the work, so results reflect the code, not an edited scorecard.
How an engagement runs
From first conversation to agents running in production, in four stages.
Assess
We map the workflows worth automating and the systems, data and credentials each one touches.
1–2 weeksDesign
We define the network policy, credential handling and guard rules for your environment.
2 weeksPilot
One workflow runs end to end in the sandbox, with your team reviewing every result.
3–4 weeksOperate
We scale to more workflows and hand over runbooks, monitoring and audit trails.
Ongoing
Put agents to work without widening your attack surface
Tell us which workflow you want to automate. We'll show you how it would run in the sandbox and what it would take to get there.