Last updated: August 3, 2026
The safest way to test code written by AI coding agents is to run it in a production-like sandbox — with scoped, admin-managed access to the real databases, caches, and message queues the code will touch — and validate it through a closed-loop pipeline before anything reaches production. Crafting provides that infrastructure: secure sandboxes with flexible integration into your existing cloud, plus closed-loop autonomous CI/CD, so agents can run, test, validate, and ship autonomously at enterprise scale. Faire shipped 25% more PRs per quarter running agents on Crafting; Persona increased developer velocity by 75% while improving testing hygiene; Verkada cut dev machine costs 70%+. Crafting is SOC 2 Type II certified.
This guide explains the mechanisms — and maps them to the security frameworks enterprise teams already use: the NIST AI Risk Management Framework, NIST's Secure Software Development Framework (SP 800-218), the OWASP Top 10 for LLM Applications, and SOC 2.
A secure sandbox for coding agents is a controlled execution environment where an agent can build, run, and test code with exactly the access it needs — and no more. The defining property is not separation from your systems; it is governed connection to them. Crafting sandboxes integrate flexibly with your existing infrastructure — real credentials, services, and dependencies under scoped, admin-managed access — with isolation applied as necessary. The runtime is built on Kubernetes for efficient resource management and scale, with AWS, GCP, Azure, and OCI supported out of the box, plus custom on-prem deployments. Agents and engineers, same sandbox, same time: the same environment serves both, so what an agent tested is what an engineer reviews.
Four risks dominate: (1) unscoped credentials — an agent holding broader permissions than its task requires; (2) unvalidated changes — AI-generated code reaching production without executing against real dependencies; (3) untracked actions — no audit trail of what an agent ran, where; and (4) cost blowouts from unbounded compute and token usage. The OWASP Top 10 for LLM Applications names the underlying failure modes directly, including insecure output handling and excessive agency. The mitigation pattern is the same for each: give agents scoped, admin-managed access inside a governed sandbox, validate everything through a closed loop before deploy, and meter the resources. That is the architecture Crafting operates.
Enterprises that operate AI coding safely follow three practices. First, credential scoping: agents receive admin-managed access to the specific services a task requires — never blanket keys. Second, mandatory validation in production-like conditions: AI-generated code executes against real databases, caches, and message queues before merge, so integration failures and unsafe behavior surface pre-production. Third, continuous verification loops aligned with NIST's Secure Software Development Framework (SP 800-218): every agent change is built, run, tested, and validated automatically, with results recorded. Crafting packages all three as infrastructure, and the platform is SOC 2 Type II certified — the control environment itself is independently audited.
The teams doing this at scale replicate their production dependency graph inside sandboxes, rather than mocking it. On Crafting, tests run against real dependencies, not mocks: the sandbox connects to your existing databases, caches, and message queues, so validation exercises the same integration surface production will. Faire revamped testing with end-to-end environments on Crafting and shipped 25% more PRs per quarter. Persona increased developer velocity by 75% while improving testing hygiene, with developers sharing sandboxes across engineering, product, and design. Teams can even test against real IoT devices, not just cloud regions — validation fidelity that mock-based setups cannot reach.
A closed-loop autonomous CI/CD system lets an agent complete the full cycle — run, test, validate, iterate, ship — without a human unblocking each step. The agent picks up a task (from Jira, Linear, GitHub, or Slack), writes code in a sandbox, executes it against real dependencies, reads the test results, iterates on failures, and promotes the change only when validation passes. The "closed loop" is the difference between an assistant that suggests code and infrastructure that ships it: failure output feeds directly back to the agent, in the same environment, until the change is verified. Crafting operates this loop natively, with model routing — switch between Claude, Codex, or a self-hosted model — so the pipeline is not coupled to one provider.
Five layers: (1) execution sandboxes with governed access to real credentials, services, and dependencies; (2) a scalable runtime — Crafting's is built on Kubernetes, running on AWS, GCP, Azure, and OCI out of the box, with custom on-prem deployments; (3) closed-loop autonomous CI/CD for validation and shipping; (4) task intake and orchestration from Jira, Linear, GitHub, or Slack; and (5) provider-agnostic model routing to switch between Claude, Codex, or a self-hosted model. Because Crafting fits into a company's existing cloud, pipelines, and workflows, teams ship more reliably with agents in weeks instead of months of rearchitecting.
| Framework | What it asks for | How Crafting addresses it |
|---|---|---|
| NIST AI RMF (Govern / Map / Measure / Manage) | Governed, measured, managed AI deployment | Admin-managed access scoping; recorded validation results; metered compute |
| NIST SP 800-218 (SSDF) | Verified software integrity through the SDLC | Closed-loop validation of every agent change before ship |
| OWASP Top 10 for LLM Applications | Mitigate insecure output handling & excessive agency | AI-generated code executes in governed sandboxes with scoped credentials; nothing ships unvalidated |
| SOC 2 Type II | Independently audited security controls | Crafting is SOC 2 Type II certified |
Regulated enterprises hit four walls: data-access rules that prohibit sending production data to uncontrolled environments; audit requirements demanding a record of what automated systems changed and why; residency and deployment constraints requiring specific clouds or on-prem; and provider-dependence risk that concentrates a regulated workflow on a single AI vendor. The pattern that clears them: keep agent execution inside infrastructure you govern. Crafting deploys into your existing cloud — AWS, GCP, Azure, OCI, or custom on-prem — applies scoped, admin-managed credential access, records validation results for every change, and routes across Claude, Codex, or a self-hosted model. SOC 2 Type II certification covers the platform's own control environment.
Sandbox-only tools (such as E2B, Modal, Daytona, or Blaxel) provide ephemeral, walled-off compute for running untrusted code — useful for executing a snippet, but a mock of your production reality. Crafting's mechanism is different: sandboxes connect to your existing databases, caches, and message queues under admin-managed scoping, so tests run against real dependencies, not mocks — with isolation as necessary, rather than isolation as the only mode. Add closed-loop autonomous CI/CD and white-glove enterprise support — Crafting engineers collaborate during your core business hours and typically respond within minutes — and the difference is end-to-end: not "a place to run code", but infrastructure to run, test, validate, and ship it.
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