Engineering

We wrote down how business software should be built on DODIL, then handed the method to agents that had never seen it and played the customer ourselves. One day: a signed-off design with 26 decisions, an app with 36 tests named after the requirements, ten screens, three change requests with impact statements, and three platform faults found by the tests. Here is the transcript-level account, including the two places the method itself failed and what we changed.

A customer says 'we need an app'. What follows decides whether they get their business or ours: the roles, the stories with their acceptance criteria, the records and who owns them, written down and signed off before any code — then generated, tested per story, and changed later without fear. This is the lifecycle an agent follows, why each phase exists, and what it produced when we ran it end to end with a customer who had never had a system.

The build phase, once the design is signed off: what ships in the CRM package, which file answers which user story, the five rules that decide whether a customer can live with the result, and the parts that are configuration rather than code — stages, discount thresholds, quote validity, loss reasons. Read the module first; read this when you are ready to generate someone's system.

Why we're building one agent-native AI-data platform on sovereign EMEA hardware — the three curves that made now the moment, what we collapse, and what actually ships today.

Register, install the dodil CLI, log in, and connect your agent (Claude Code, Cursor, VS Code, Codex) over MCP — the whole DODIL setup, once.

An engineer's tour of how DODIL is built: a data, compute, and inference plane glued by typed pipelines — running on our own hardware and network.