Codex use cases

Prototyping & DesignCoding & Engineering

Use AI to close the gap between design and code by bringing production context into design work and turning design intent into more implementation-ready code changes.

Outcome: Shows how product teams can reduce handoff loss between designers and engineers. Instead of treating Figma and the codebase as separate worlds, the workflow helps design reflect what is actually in production and helps code changes preserve the intent of the design.

Coding & Engineering

Uses a model-vs-model development workflow where one AI agent builds the first version and another reviews the output for bugs, edge cases, production readiness, and missed requirements.

Outcome: Shows how AI-assisted coding can become more reliable when creation and critique are separated. One model produces the work, another challenges it, and the human decides which feedback is worth acting on.

Coding & Engineering

Uses advanced Codex workflows to run multiple engineering tasks in parallel, keep work organized with Plans.md, and route generated changes through GitHub review before merging.

Outcome: Shows how AI coding tools become more useful when paired with planning files, isolated worktrees, and normal code-review habits. Instead of one linear chat, the workflow turns Codex into a structured collaborator that can explore several tasks without tangling the codebase.

Company AI AdoptionBusiness Operations

Built a company-wide AI adoption system at Delight.ai with an internal automation marketplace, gamified quests, AI usage tiers, secure app templates, and personal knowledge workflows like The Gardener.

Outcome: Turns AI adoption from a top-down mandate into an internal product. Employees can request automations, team up to build them, earn rewards, ship on secure templates, and see a clear path from beginner to advanced AI use.

Coding & EngineeringBusiness Operations

Uses Notion AI, Codex, GitHub, Slack, Honeycomb, and Whisper to make engineering work more structured: preparing standups, turning specs/comments into PRs, and giving engineers better context before they start building.

Outcome: Shows how AI can improve engineering velocity without skipping the parts that make software teams work well: shared context, written specs, reviewable changes, observability, and human judgment.