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agent-sandbox

I built this to let an AI coding agent run real infrastructure commands against a real Kubernetes cluster — without ever holding a standing credential, and without being able to destroy anything unsupervised.

Three MCP tools. Every call runs inside a gVisor-sandboxed Kubernetes Job with a short-lived, narrowly-scoped credential I have Vault mint for that single action. Anything destructive stops at a human approval gate.

AI Agent (Claude Desktop / Cursor)
        │  MCP protocol (stdio)
        ▼
┌──────────────────────────────────────────┐
│  MCP Server            src/agent_sandbox │
│    3 tools -> guardrails -> broker ->    │
│    sandbox -> audit                      │
└───────┬──────────────────────┬───────────┘
        │                      │
        ▼                      ▼
┌────────────────┐   ┌──────────────────────────┐
│ Credential     │   │ Sandbox Runner           │
│ Broker (Vault) │   │ K8s Job + gVisor         │
│ 10-min leases  │   │ restricted PSS           │
│ per-action     │   │ default-deny NetworkPolicy│
│ scope          │   │ cpu/mem limits, deadline │
└────────────────┘   └──────────────────────────┘
        │                      │
        └──────────┬───────────┘
                   ▼
        ┌──────────────────────┐
        │ Guardrails + Approval│
        │ policy.yaml, SQLite, │
        │ agent-sandbox CLI    │
        └──────────────────────┘

Why I built this

An AI tool I was using once proposed a Terraform change against production infrastructure that would have forced replacement of a live resource. The plan looked routine. The failure mode wasn't the model being wrong — it was that nothing sat between a plausible-looking plan and a destructive apply.

I built this project as the missing layer, in working code:

  • the agent never holds a credential it could reuse

  • everything runs somewhere it can't hurt the host

  • destructive changes stop and wait for a person

  • every action is on the record

make demo reproduces the exact scenario I ran into. A one-line label edit forces replacement of a running Deployment, and the gate catches it.

Related MCP server: enterprise-agent-lab

Quick start

Requires Docker, kind, kubectl, vault, terraform, and Python 3.11+.

brew install kind kubectl hashicorp/tap/vault terraform
make up      # ~5 minutes from cold: cluster, CNI, gVisor, Vault, image, verify
make demo    # the forces-replacement guardrail demo
make down    # tear it all down

make up is idempotent. It finishes by running make verify, which proves the isolation claims rather than asserting them (see below).

Point an agent at it

cp examples/claude_desktop_config.json \
   ~/Library/Application\ Support/Claude/claude_desktop_config.json

Cursor: copy examples/cursor_mcp.json into .cursor/mcp.json. Then ask the agent to "check pod status in demo-app" or "plan the k8s-demo terraform".

The three tools

Tool

Risk

Behaviour

k8s_get_pod_status(namespace)

low

Runs immediately. Credential scoped to get/list/watch pods in one namespace.

terraform_plan(working_dir)

low

Runs immediately. Saves the plan so a later apply executes exactly the reviewed diff.

terraform_apply(working_dir, approval_id?)

high

Without approval_id: computes the plan, records a pending approval, applies nothing. With one: spends the approval and applies the saved plan.

The four components

1. Sandboxed execution — src/agent_sandbox/sandbox.py

One throwaway Job per tool call. Every control is there for a specific reason:

Control

Prevents

runtimeClassName: gvisor

Syscalls hit the gVisor sentry, not the host kernel

PSS restricted, enforced by the API server

root, privilege escalation, capabilities, writable rootfs

automountServiceAccountToken: false

Any ambient cluster identity inside the sandbox

default-deny NetworkPolicy + API-server allowlist

Internet egress, lateral movement, metadata endpoints

resources.limits, activeDeadlineSeconds

A runaway job starving the node or hanging forever

backoffLimit: 0

A failed destructive action being silently retried

The credential is mounted as a file, never an env var — env vars leak through kubectl describe, /proc, and crash dumps.

2. Credential broker — src/agent_sandbox/broker.py

cred = broker.issue_scoped_credential("k8s_get_pod_status")
# -> Vault mints a ServiceAccount + Role + RoleBinding, 10-minute lease
# -> revoked immediately after the Job finishes
  • The agent never picks its own scope. Scope is derived from the action.

  • Deny by default. An action with no mapped scope gets no credential.

  • Blast radius is enforced. Requesting any namespace but the target is refused.

  • The token never leaves the module. Credential.__repr__ prints token=<redacted>, so even an accidental log can't leak it.

I verified this by hand: a pod-reader token lists pods in demo-app, is denied in kube-system, is denied on secrets, and stops working the moment its lease is revoked — leaving no ServiceAccount behind.

3. Guardrails — policy/policy.yaml, src/agent_sandbox/guardrails.py

Deny by default: registering an MCP tool is not enough to make it callable. A tool absent from the policy is refused, so adding capability requires a deliberate risk-tier decision.

Approvals are hardened against the obvious attacks:

  • single-use — consumed inside one SQLite transaction, so two concurrent applies can't spend the same approval

  • parameter-bound — bound to a hash of the exact tool + parameters, so an approval for k8s-demo can't be replayed against prod-cluster

  • expiring — 30 minutes by default

  • out-of-band — granted via a separate CLI process. There is no MCP tool to approve anything; the agent has no code path to approve its own request.

4. MCP server — src/agent_sandbox/server.py

Built on the official Python SDK (mcp 2.0, MCPServer). The transport layer is deliberately thin and grants no authority of its own — a bug there cannot widen what the agent can do, because the policy and the API server's Pod Security admission are the actual controls.

Audit log

Every call emits a correlated event trail to var/audit.jsonl:

tool.request -> guardrail.decision -> credential.issued -> sandbox.started
   -> sandbox.completed -> credential.revoked -> tool.result
make audit
./.venv/bin/agent-sandbox audit --request-id req-4239b8bb5459 --json

Credential values are scrubbed recursively before write; scope, lease id and TTL are kept. A test asserts no JWT-shaped string ever reaches the log.

Verified, not assumed

Two things in this project are easy to claim and quietly not have, so I didn't take them on faith. make verify tests both against the live cluster:

== 1. gVisor kernel check ==
     kernel reported: Linux version 4.19.0-gvisor
  PASS: sandbox runs on the gVisor sentry kernel
== 2. NetworkPolicy egress enforcement check ==
  PASS: baseline connectivity works (got PONG)
  PASS: default-deny egress enforced (traffic blocked)

This caught a real problem while I was building it. kind's default CNI (kindnet) accepts NetworkPolicy objects and silently ignores them — I applied a default-deny egress policy and pod-to-pod traffic still got through. The sandbox would have looked locked down while having full network access. I fixed it by disabling kindnet and installing Calico, which enforces for real. See scripts/install-calico.sh.

I hit a related trap allowlisting the API server: the ClusterIP doesn't work, because kube-proxy DNATs to the real endpoint before Calico evaluates egress. The symptom was a sandbox that just hung with no policy-denied event to explain it. Documented in scripts/apply-sandbox-policy.sh.

Honest limitations

  • gVisor runs, but this is still kind. I installed runsc inside the kind node (a container in Docker Desktop's Linux VM) and verified it's active. That's a real gVisor sandbox, not a production-hardened node.

  • The AWS/STS path is conditional. scripts/vault-setup.sh only configures Vault's AWS secrets engine when real AWS credentials are present; without them it's skipped and says so. I didn't want to fake that path just to make the demo look complete. The live, demonstrable credential path is the Kubernetes one, which is fully real: dynamic ServiceAccounts, real RBAC, real leases, real revocation.

  • Vault runs in dev mode — in-memory, root token root, no seal. Fine for a local project, not something I'd deploy as-is.

  • Destructive-signal detection is string matching on plan output. It's a surfacing aid for the human, not a security boundary — terraform_apply is already high-tier and gated regardless of what the scan finds.

  • Single-node cluster, so the PVC holding Terraform state is ReadWriteOnce on one node.

Layout

cluster/      kind config, RuntimeClass, namespaces, RBAC, network policy
images/       sandbox runner image (terraform + kubectl, providers vendored)
policy/       guardrail policy: risk tiers and destructive signals
scripts/      up/down, gVisor + Calico install, verification, demo
src/          the package: broker, sandbox, guardrails, approvals, audit, MCP
terraform/    demo module managed by the agent
tests/        56 unit tests + a real-stdio MCP integration check

Testing

make test       # 56 unit tests, no cluster required
make test-mcp   # drives the server over real MCP stdio (needs the stack up)
make verify     # proves gVisor + NetworkPolicy enforcement on the live cluster

Available Tools

3 tools
k8s_get_pod_statusGet pod statusA

Read-only. Lists pods and their phase in the target namespace, executed inside a gVisor sandbox with a credential scoped to get/list/watch pods in that one namespace. Runs immediately; no approval needed.

ParametersJSON Schema
NameRequiredDescriptionDefault
namespaceNodemo-app

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A3.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full disclosure burden and does it well: it states read-only semantics, gVisor sandbox isolation, the exact credential scope (get/list/watch on one namespace), and that no approval gate exists. It stops short of pagination or error behavior, but the safety and permission profile is unusually well covered for an annotation-free tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short sentences, front-loaded with the safety-critical 'Read-only.' qualifier followed by scope, environment, and approval status. Every sentence adds information; no filler or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return-format explanation is unnecessary, and the description covers read-only status, execution environment, credential scope, and approval behavior for a single-parameter tool. The one remaining gap is that the namespace default and format are never mentioned in either the description or the schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for the lone parameter. It only gestures at 'the target namespace' without naming the parameter, giving the default value ('demo-app'), or indicating accepted namespace formats. The agent must open the schema to learn anything actionable about the input.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb and resource ('Lists pods and their phase') plus an explicit scope ('in the target namespace'), so the agent knows exactly what is returned. It does not differentiate from siblings, but the siblings (terraform_plan/terraform_apply) are an unrelated domain, so no routing confusion exists. Minor tension: the name says 'pod status' (singular) while the description lists pods (plural).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

'Read-only' and 'Runs immediately; no approval needed' imply when this is safe to call, and the namespace scoping implies the context. There is no explicit when-to-use/when-not statement or named alternative, so usage is only implied rather than stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

terraform_applyTerraform applyA

HIGH RISK -- mutates real infrastructure. Calling without approval_id does NOT apply anything: it computes the plan, records a pending approval, and returns an approval id for a human to review out-of-band. Once a human has approved, call again with that approval_id to execute the reviewed plan. Approvals are single-use, expiring, and bound to these exact parameters.

ParametersJSON Schema
NameRequiredDescriptionDefault
approval_idNo
working_dirYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden and does so well: it flags HIGH RISK mutation, explains that the no-approval path is non-destructive (computes plan, records pending approval), and discloses approval lifecycle traits (single-use, expiring, parameter-bound). This is far beyond what a bare 'apply' would convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Four tight sentences with the risk warning and the two-phase contract front-loaded; no filler. Dense but readable, and every clause adds operational information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Output schema exists so return-value documentation is unnecessary, and the description still names the key return (approval id). Combined with the risk warning and approval lifecycle, an agent has everything needed to call this correctly and safely.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate; it fully explains approval_id semantics (optional, single-use, expiring, must match exact parameters, absence means no apply). working_dir is never explained, which is the one remaining gap, but the high-risk parameter is covered.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('mutates real infrastructure', Terraform apply) and immediately clarifies its two-phase nature, which separates it from terraform_plan's pure-preview role. An agent can tell what this does and when it would fire without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit conditional workflow: call without approval_id to compute a plan and register a pending approval, then call again with the returned approval_id to execute. It also states the human review happens out-of-band, so the agent knows not to wait on this call for the mutation to occur.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

terraform_planTerraform planA

Computes a Terraform diff for a module under the terraform/ root. Runs immediately in the sandbox and persists the plan so a subsequent apply can execute exactly the reviewed diff. Output is annotated when the plan contains destructive changes such as forced replacement.

ParametersJSON Schema
NameRequiredDescriptionDefault
working_dirYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does well: it discloses immediate sandbox execution (no confirmation gate), that the plan is persisted as durable state, and that output is annotated on destructive changes like forced replacement. It omits auth/permission requirements and failure behavior, which keeps it out of 5 territory.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three tight sentences with no filler, front-loaded with what the tool does before the sandbox/persistence and destructive-change details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need not be explained, and the description covers execution model, persistence, and destructive-change signaling. The main residual gap is parameter semantics for working_dir, which neither schema nor description pins down.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% for the single working_dir parameter, so the schema adds nothing. The description hints that the module lives 'under the terraform/ root', which weakly constrains the path, but it never states whether working_dir is absolute, relative, or relative to that root.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Computes a Terraform diff for a module'), and scopes it to the terraform/ root. It also implicitly distinguishes itself from the sibling terraform_apply by framing itself as the review step that a 'subsequent apply' consumes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives clear context for use: it runs immediately and produces a persisted plan that a later apply executes, which tells the agent this belongs before terraform_apply. It stops short of explicit when-not-to-use guidance or naming the alternative tool directly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 3 tool updatesv0.1.0
    • First observedk8s_get_pod_status
    • First observedterraform_apply
    • First observedterraform_plan

TDQS

A3.9/5.0

Scored across 3 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: read-only Kubernetes pod status, a Terraform plan diff, and a gated Terraform apply. The plan/apply pair is explicitly differentiated by the approval workflow, so an agent can reliably choose the right tool.

Naming Consistency4/5

Names use consistent snake_case with a domain prefix and a verb (k8s_get_pod_status, terraform_plan, terraform_apply). The k8s tool adds an object noun while the Terraform tools do not, a minor deviation but still predictable.

Tool Count3/5

Three tools across two distinct domains (Kubernetes and Terraform) is thin for an agent sandbox. Each tool earns its place, but the surface feels minimal relative to the apparent infrastructure-management scope.

Completeness3/5

Kubernetes coverage is limited to reading pod phase with no logs, events, describe, or any mutating operation, and Terraform lacks init/validate, destroy, and state inspection. The core plan-then-apply lifecycle is well covered, but notable gaps remain for a sandbox meant to work with real infrastructure.

Maintenance

ActivityMaintained
ResponsivenessNo issues

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