Use Case · Engineering

Preview environments on the Software Lifecycle Cloud

Preview environments on KERNSTACK are targets of the same declaration: point stack.target at a preview name (or let an agent branch bring its agent-<id> environment) and the whole project — routes, functions, tables, buckets, workflows — materialises with production’s shape and rehearsal data. Review stops being a description of the change and becomes a click-through of the change, running the stack it will run in production.

stack.json IaCHosted GitAgent SandboxesEdge Gateway
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The engineering bottleneck with assembled stacks

Reviewers get screenshots and promises because staging is a single shared snowflake that never matches production or the branch under review. Data-dependent bugs survive review (“worked on staging”), infrastructure drift hides until deploy, and agent-made changes arrive as diffs nobody ran. The review ritual consumes the time and skips the verification.

Architecture on the Software Lifecycle Cloud

The stack is a file, so an environment is an instantiation: same stack.json, different target, seeded from seed files that ship with the repository (seed/tables and seed/buckets upload after deploy). Branch previews run the branch’s stack; agent branches deploy to agent-<id> environments automatically in code mode. Expiry is a target you delete, and its tables of rehearsal data go with it — no shared staging state to curate.

Seeded, not copied from production

Previews should rehearse shape, not photocopy customers. Seed data is declared with the repo — representative tenants, products, edge cases somebody once hit — so a preview starts from known state in seconds and shows the same fixture to every reviewer. Production data stays in production; the preview proves behaviour, and the plan for production proves the change.

  • A branch preview is the branch’s actual stack — tables and workflows included, not a frontend with mocks
  • Agent changes arrive with a live agent- URL; reviewers click before they read the diff
  • Teardown deletes the target and its rehearsal data together; preview sprawl has an off switch

Costs of honesty

Each live preview consumes its own small meter while it runs — storage for its seeded tables, compute when clicked. That is the correct incentive: previews are cheap, idle previews cost almost nothing, and the bill itemises environments nobody remembered to close. Platform tiers of “unlimited previews” hide that signal; projects here can see it.

FAQ

Frequently asked questions

By deploying the stack to another target: kernstack deploy with the preview’s stack.target. Seed files in the repository populate its tables and buckets automatically after deploy, so the environment arrives rehearsed, not empty.

No — deliberately. Seeds are declared fixtures, representative rather than real. Review verifies behaviour and the production plan verifies the change itself; customer data never becomes review material.

The project, visibly: each target’s storage and compute land on the same credit balance. Idle previews spend near zero, and deleting the target removes the cost with the environment, fixtures included.

Connected in the Project Graph

Related capabilities, architectures, and comparisons

Category PillarThe Software Lifecycle CloudWhy people and agents build, deploy, and operate on a single vertically integrated stack.Service · Manifeststack.jsonOne manifest for the whole projectService · SourceHosted GitRepository hosting inside the projectService · AgentsAgent sandboxesIsolated sandboxes for coding agentsService · EdgeGatewayHTTP, WebSocket, and prefix routesIndustry SolutionE-commerce Cloud SolutionsE-commerce on KERNSTACK owns the parts of commerce that constitute the business: the catalogue and its search, carts and checkout state, the order lifecycle, raw payment events reconciled into provable order states, and fulfilment workflows with human steps for the exceptions. Cards stay with the payment provider; the data that makes the business worth buying moves onto infrastructure the merchant controls, in Europe.Use Case · ProductExperiment assignmentAssignment is a pure function of the subject key and the experiment’s salt — stable across requests, no coordination needed. The function records the assignment row once and serves the variant; outcome handlers were already writing events, which now simply carry the experiment id the assignment returned. Analysis reads assignments and outcomes from neighbouring tables with the same keys.Use Case · AgenciesAgency client deliveryThe studio’s delivery shape is a template stack.json: gateway, functions or workers in the client’s stack languages, tables, buckets, and apps under one target per client, on European infrastructure the studio can name in the pitch. Previews run the same declaration; the client’s approval happens in the bound conversation with a live link. Every client’s meter is separate by construction — the studio sees delivery cost per client without allocation spreadsheets.

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