The Software Lifecycle Cloud.

Why the modern software stack broke when AI agents arrived

Over the past decade, shipping a single digital product required assembling a collage of roughly twenty tools across four disconnected layers: Slack and Jira for planning, GitHub and CI pipelines for code, Cursor or Claude Code with external model keys for intelligence, and a dozen AWS or SaaS primitives (Lambda, Fargate, DynamoDB, OpenSearch, S3, CloudFront, PineCone, Auth0, PostHog) for runtime infrastructure.

Coding agents accelerated the act of writing application code by an order of magnitude, but they exposed the fragility of the assembled collage. Handing an autonomous agent thirty cloud API keys and IAM policies is an operational hazard; keeping the agent trapped in a local chat window means humans still spend 80% of their week copying logs, wiring Terraform, and babysitting preview deployments.

The Software Lifecycle Cloud resolves this contradiction by designing infrastructure, code, intelligence, and collaboration as one vertically integrated system from the hardware up.

• One declarative file (stack.json) defines frontend apps, routes, functions, workers, tables, buckets, search indexes, inference endpoints, and workflows.
• One identity and permission ceiling governs human engineers, external partners, and sandboxed AI agents.
• One credit balance meters compute, storage, network egress, search, training, inference, and agent runs with true scale-to-zero.

The four integrated layers of KERNSTACK

Unlike PaaS wrappers that rent Amazon EC2 underneath and resell database add-ons, KERNSTACK is engineered from scratch on dedicated European bare metal around four tightly coupled layers that read and write to the same project graph:

1. Infrastructure Layer: WebAssembly HTTP/WebSocket functions, container workers (Go, Node.js, Python, PHP), serverless NoSQL tables with change streams, object storage buckets with mountable volumes, native full-text and HNSW vector search, serverless catalog and custom model inference, training and fine-tuning as worker jobs beside the data, human-approval workflows, and built-in product analytics.

2. Code Layer: Hosted project git with large-file pointer storage, declarative stack.json infrastructure-as-code, deterministic kernstack plan diffs, and automatically vendored SDKs (.kernstack/sdk--v1).

3. Intelligence Layer: Isolated agent sandboxes where harnesses like Cursor, Claude Code, Codex, and Pi operate on dedicated agent/ branches and preview environments under a strict credit ceiling.

4. Collaboration Layer: Project conversations and a four-column task board (Backlog, Active, Review, Done) accessible via the web console, Telegram, or WhatsApp — allowing clients and domain experts to review live @agent previews without ever touching cloud credentials.

The compounding moat: the living project graph

When conversation, tasks, code, infrastructure, and production telemetry live in separate SaaS silos, institutional context evaporates at every handoff. On KERNSTACK, every conversation message, board task, git commit, stack.json diff, preview URL, workflow approval, and production analytics event is a node in the same project graph.

The result is a compounding loop: a customer reports an edge case in Telegram, @agent correlates the message with the production table schema and function logs, proposes a fix on an isolated branch, deploys a live preview environment, and posts the verification link back to the thread. An engineer reviews the kernstack plan diff and merges to production.