/5 CONNECTED STEPS
Make an AI coding change with working checks
Map a requested change, implement its controls and prove the actual user path handles the target failure.
Download complete workflow .md ↓Use with your AI
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What you need
An editable repository, a concrete change, agent configuration and an isolated way to exercise behavior.
Why these fit together
The component map locates the real implementation; design chooses observable controls; registration makes them run; seeded failures show they detect the intended problem.
Follow this order
Map an app workflow to running components
Locate each step, runtime and state transition.
Choose guard types before building a feature
Select a control for each concrete failure.
Build AI guardrails that actually run
Register guards in their actual execution paths.
Prove checks with deliberate failure fixtures
Exercise bad input, clean input and broken scans.
Match verification evidence to bug behavior
Verify the reported defect on its real consumer surface.
Give this task to your AI
Use the Make an AI coding change with working checks workflow. My task and inputs are supplied below. Read the prerequisites and check your available tools. Follow the core steps in order, passing the actual output of each step to the next. Adapt installation and commands to this environment; do not copy another agent's configuration blindly. If a required tool or input is missing, identify it before that step. Run the completion check and report observed results and unresolved work.
Setup to provide or build
A selected agent/runtime adapter, example repository, registered checks and an executable demonstration task.
Check the result
The real entry point catches a seeded defect, accepts valid input and reports an unreadable check as a failure rather than success.
Curated implementation workflow. End-to-end execution must be verified in your environment.
Optional additions
Load these only when your task needs them.