Vision
The governance runtime
for autonomous automation.
Agents are a new class of autonomous actor. Just as human users required IAM and services required service mesh, agents require their own identity, capability, and governance infrastructure. This platform provides that layer.
A new kind of actor
We used to have software — deterministic, decision-less, does what it's told. And we had users — people who operate software, write code, make judgments, and bear consequences.
An AI agent is something in between. It runs on an OS like software. But it reasons, writes its own code, and decides what to execute — like a user. It is both, and neither existing governance model fits.
Software doesn't need identity or permissions — it runs under the identity of whoever deployed it. Users don't need sandboxes — they have judgment, reputation, and bills to pay. An agent needs all of it: the execution capability of software, the identity and permission model of a user, and a governance boundary that accounts for the fact that it has no intrinsic motivation to behave — and can be influenced by malicious actors far more easily than a human.
The paradigm shift
When you hire a new employee, you don't disable their internet access because you don't trust them yet. They need connectivity to do their job. Instead, you give them a real workstation with real tools, and you govern through scoped permissions, access policies, activity logs, and approval workflows.
This is how all modern security works: Unix processes are powerful, the kernel controls what they can reach. Containers are powerful, network policy controls what they can call. Cloud IAM does not limit what code can do internally — it limits what it can access.
The pattern is universal: full capability inside, governed boundary outside.
Approach A — Restrict the agent
- Force agents to use predefined tool APIs
- Only allow structured function calls
- Limit what libraries and SDKs agents can use
- Hope that prompt discipline is enough
Security achieved by reducing capability.
Approach B — Give broad access
- Run agents in containers with wide permissions
- Pass API keys directly into the environment
- Log informally or not at all
- Rely on human supervision to catch problems
Capability preserved, but ungovernable at scale.
Both approaches fail because they treat agents as either dumb software (restrict everything) or trusted users (restrict nothing). Agents are neither. They need the execution freedom of software running on an OS, combined with the identity, permissions, and audit trail we expect of any actor with decision-making power.
The solution
Agents run normally — any container, any framework, any code — inside isolated sandboxes with full operating system access. They use standard libraries, SDKs, shell scripts, and real APIs. No artificial tool interfaces required.
All external interaction is mediated by a Control Boundary: a runtime-enforced governance layer that wraps every agent and governs everything it can affect outside its sandbox.
Architecture
Each agent workspace is a Kubernetes pod with three containers: an init container that sets up network interception, the agent itself (full OS, bash, LLM reasoning), and a lightweight TCP sidecar that tags every outbound connection with the agent's identity token.
All traffic flows to a per-node external proxy (DaemonSet) that handles TLS interception, OPA policy evaluation, credential injection from Vault, budget tracking, DNS control, and structured audit emission. The agent never talks to the internet directly.
The security model splits trust explicitly. The sidecar inside the pod is a dumb TCP forwarder — no secrets, no policy, no TLS keys. All intelligence lives on the external proxy, which the agent cannot reach or compromise without a container escape.
This is defense in depth: container isolation, network policy, cryptographic identity, and credential management are all independent layers. Failure of one does not compromise the others.
The agent as amplifier
The governance boundary does more than make agents safe. It makes every workload more capable.
Consider running a service. In the simplest case, the agent just starts the binary and reports back when it exits. Traditional automation. But because a reasoning layer exists, you can write a playbook:
Your cron job just became an autonomous incident resolver. And because the governance boundary exists, this is safe — the agent can only reach the APIs it's been granted, credentials are injected transparently, every action is audited, and budgets prevent runaway behavior.
The main agent doesn't need Slack credentials or email access. It spawns a
notifier
agent with those specific capabilities. The service-runner stays scoped to its job.
Least privilege becomes composability.
Teams start at the simple end and slide toward autonomy as confidence builds — without re-platforming, because the same governance model applies across the entire spectrum.
Why existing tools fall short
Agent frameworks
LangGraph, CrewAI, AutoGen, Semantic Kernel — focus on how agents think and coordinate tasks. Assume the execution environment is trusted. Governance lives in application code, not at the infrastructure boundary.
Sandbox providers
E2B, Google GKE Agent Sandbox — provide compute isolation and basic lifecycle management. The boundary is: "we give you a safe box; what you do inside is your problem." No capability tokens, no egress interception, no credential brokering, no unified audit.
Workflow engines
Camunda, Temporal, Airflow — orchestrate business processes where agents are steps in a workflow. Do not treat agents as first-class autonomous actors with identity and scoped authority.
Cloud providers
AWS, GCP, Azure have the primitives (IAM, VPC, egress gateways, Secrets Manager, CloudTrail) but do not yet offer a unified abstraction for governing autonomous workloads as actors. Building on their primitives requires assembling 8+ services and significant glue code.
This platform is not another agent framework or orchestration tool. It is the execution substrate that governs how autonomous systems interact with the world. It sits underneath frameworks and workflow engines, not alongside them.
Built on proven infrastructure
See it in action
We'll walk you through the live canvas, the governance boundary, and how an agent runs with full power and full control.