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Building AI-Native Workflows1 / 2

1 min lesson

Encode the team’s standards

Imagine this comes up at work: "A customer complains the agent keeps using the wrong logging library and ignoring their error-handling pattern across many tasks. What's the most durable fix?" Start with the practical move.

Step 1 of 2

Encode the team’s standardsrules / .cursorrules as a workflow artifact

A customer’s conventions are part of the spec, not a nice-to-have. Cursor lets you commit rules (the .cursorrules lineage) so every agent run inherits the team’s standards without you restating them in each prompt.

.cursor/rules - conventions encoded once, applied to every run.
# Stack & conventions
- Backend: Python 3.12, FastAPI. Frontend: TypeScript, React.
- Use the repo's `Result` type for fallible calls; never raise across module boundaries.
- All new endpoints get a pytest test and an OpenAPI entry.

# Editing rules
- Match existing import ordering; do not reformat untouched lines.
- Prefer small, anchored edits over rewriting whole files.
- If a public type changes, update its callers in the same change.
Rules are versioned context

Conventions in a committed rules file are reviewable, diff-able and shared across the whole team’s agent runs.

That turns “the model keeps using the wrong logger” from a recurring prompt fix into a one-line PR.