Use cases

How teams typically deploy VaultRun. These are reference patterns — not named customer case studies — based on the product’s real capabilities (sandboxes, MCP, local inference gateway, CI runner, Enterprise SSO).

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01 / platform engineering

Shared sandbox platform for AI agents

Problem. Agents need to run untrusted code and touch files. Letting them loose on shared VMs or developer laptops creates blast-radius and audit gaps.

Pattern. One VaultRun API per environment. Agents create a session, upload tools/scripts via API or MCP, run commands in an isolated container, then tear down. Network stays off unless explicitly allowlisted.

Outcome. A controlled execution plane that stays on your network — no SaaS telemetry, no third-party code runner.

02 / regulated / security-minded AI

Agent access to data and cloud without ambient credentials

Problem. Giving an LLM toolchain raw AWS/DB credentials is risky; banning tools blocks useful workflows.

Pattern. Use MCP tools with explicit opt-in (e.g. AWS tools only when enabled), scoped filesystem allowlists, and Secrets Manager / Vault injection per run — results redacted in audit where needed.

Outcome. Capability with a paper trail — suitable as an internal control story for security review.

03 / engineering / CI

PR tests inside an isolated VaultRun sandbox

Problem. CI runners that share state or host Docker sockets leak secrets and flake under load.

Pattern. Point a GitHub webhook at ci-runner. Each PR clones into a VaultRun session, runs your test command list, posts a PR comment + commit status, optionally Slack/Teams.

Outcome. Homogeneous isolation for humans, agents, and bots.

04 / platform / workflows

Workflow-as-asset: save, verify, replay

Problem. Agent wins live in ephemeral chats. Teams cannot re-run a proven plan, prove what passed, or attribute spend to a named workflow.

Pattern. Capture successful tool sequences as VaultRun missions on your infra. Attach verify checkpoints after runs, export sealed evidence digests when you need a portable proof, keep agent notes in sandbox memory, optionally describe swarm edges for multi-agent sessions, and snapshot cost attribution when you replay.

Outcome. Workflows become inspectable assets with exportable proof, not disposable transcripts.

05 / local AI

Local models that can act — without a SaaS runner

Problem. Ollama / LM Studio can chat. Giving them shell or tools on the host is an open blast radius; shipping execution to a cloud sandbox fights the reason you went local.

Pattern. Run vaultrun-local as an OpenAI-compatible gateway. Clients (Open WebUI, Continue, LiteLLM) talk to your local model; tool calls execute in VaultRun sandboxes. Successful loops can auto-capture as missions; optional OpenJEV completion gates block “done” without evidence.

Outcome. Local AI with a controlled action plane — think local, act contained.

06 / verify / OpenJEV

Stop agents from declaring victory without evidence

Problem. Agents narrate success. Deterministic checks catch missing files and bad exits; they do not judge whether a natural-language claim matches the artifacts.

Pattern. Run VaultRun verify → export sealed evidence → gate with OpenJEV or TypeSafe Jev (jev_gate / mission step jev_on_fail). Threshold miss → fail or hold.

Outcome. Completion only when claims survive evidence — not when the model sounds confident.

get enterprise → schedule a walkthrough → clone the core