# AI assistance

This project was built with help from AI coding tools. The main assistants were
Codex CLI and Claude Code CLI, using GPT-5.6 Sol and Sonnet 5.0.

Some code, configuration and documentation were drafted by these assistants.
Other parts were written by me. I defined the architecture, requirements and
constraints, decided what belonged in the project, reviewed the generated
changes, tested them on the real infrastructure and kept responsibility for the
final result. AI output was treated as a draft or implementation proposal, not
as proof that something worked.

Debugging was mixed. Some problems were investigated manually with logs,
metrics and command-line tools. Some research used Google and official
documentation. Other issues were worked through with AI, including longer Codex
loops that inspected a failure, changed one thing, ran the verification again
and continued until the real system reached the expected state.

Most failures were not complicated application bugs. They were networking or
small configuration mistakes: firewall rules between Kubernetes nodes,
permissions on pod log files, an incorrect image tag, missing RBAC verbs,
stale credentials after a database reset, or monitoring windows that still
contained earlier errors. Those failures were only considered fixed after the
relevant test, deployment, metric query or health check passed.

No assistant was allowed to invent successful results. The repository roadmap
uses partial status for work that still needs external hardware or credentials,
and `docs/evidence.md` records the important checks that were actually run.