AI-Assisted Clean Code Workshop

Work hands-on with coding agents in a real codebase: uncover hidden risks, create behaviour-focused tests, refactor safely, establish repository guardrails, and coordinate multiple agents while preserving one coherent design.

AI coding tools can generate implementations, tests, and refactorings within minutes. The harder question is whether those changes are correct, understandable, appropriately structured, and safe to keep.

Generated code can compile, pass its tests, and still be wrong. It may rely on an unsupported API, silently change behaviour during a refactoring, copy a legacy workaround as if it were the preferred design, omit important failure or authorization paths, or produce an impressive test suite that protects implementation details instead of the behaviour that matters.

This hands-on workshop turns Clean Code judgement into a practical AI-assisted development workflow. Participants work with a provided application, inspect real generated diffs, improve tests, refactor code in small controlled steps, make deliberate domain and architecture decisions, and convert repeated review findings into reusable repository guardrails.

The focus is on the quality of an individual AI-assisted change. Participants begin with an unguided agent implementation and progressively make it safer, clearer, better tested, and more consistent with the intended direction of the codebase. Clean Code practices, code smells, test design, domain modelling, and architecture patterns are used as practical decision tools rather than as abstract catalogues.

The workshop is deliberately tool-agnostic. Participants may use Codex, Claude Code, Cursor, GitHub Copilot coding agents, or equivalent repository-level tools. The focus is not on collecting prompts or maximizing the number of agents. The focus is on making engineering judgement explicit, verifiable, and reusable so that AI assistance improves the codebase instead of accelerating technical debt.

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