Most failures of autonomous agents aren't model quality but uncontrolled access: arbitrary file reads, stolen keys, or unexpected outbound calls. This project treats agent orchestration as a security-first runtime problem — giving each agent a tightly scoped sandbox, policy-checked egress, and managed credentials so agents can act without becoming an attack surface.
What Sets It Apart
- Kernel-level enforcement with sandbox supervision: policies are enforced from filesystem and syscalls up to network egress, so granted capabilities are enforced by the runtime rather than only by agent code. This reduces the risk of accidental or malicious data access.
- Declarative, auditable policies plus formal verification: policies are authored as YAML and policy changes are analyzed before being applied, surfacing risky expansions (new hosts, new API methods) and requiring review. That makes policy evolution safer for long-lived agent fleets.
- Managed credential providers and inference routing: credentials are injected only for approved endpoints and inference API calls can be routed or stripped of caller secrets, enabling safe use of external LLMs or local GPUs without leaking keys.
- Hot-reloadable network & inference rules and multi-runtime support: network and inference sections can be updated on a running sandbox; the gateway supports Docker, Podman, MicroVMs, and Kubernetes so it fits local dev and cluster deployments.
Who It's For and Tradeoffs
Great fit if you run autonomous or long-running agents that must access code, data, or APIs but you need strong isolation, auditability, and credential controls (DevOps teams, research labs, platform builders). Look elsewhere if you only need a lightweight CLI client for interactive LLM chats or if you can’t run a local gateway/runtime (the system expects a host runtime like Docker/Podman or cluster infrastructure). There is operational overhead: running the gateway/supervisor and maintaining explicit policies is deliberate work traded for stronger, auditable safety guarantees.
Where It Fits
Acts as AI infrastructure between agent code and the outside world: think of it as a policy-enforced runtime layer for agents, complementary to model providers, agent frameworks, and orchestration pipelines. It’s most valuable when agent behavior needs to be constrained, monitored, and evolved safely.
