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Shouheng, AI governance and trusted boundaries

FIELD GUIDE / PRODUCTION

Before an AI pilot goes to production, close these five gaps first.

A pilot proves that a path is potentially feasible; a production system also needs to operate reliably with real users, real permissions, and continuously changing data. Use the checklist to identify gaps first, then decide on the launch.

Five launch checks

Each one must have an assigned owner and verifiable evidence. 'Completed' without evidence is still pending.

01 Business goals and current baseline

Describe clearly who does what task, the current handling method, and the success criteria. Record sample size, task difficulty, and the amount of human involvement; do not use carefully selected best results from demos to represent all business.

02 Identity, Permissions, and Data Boundaries

Test the same questions and tools with different roles. Check whether retrieval, caching, downloads, logs, and system writes follow the same boundaries; test whether there are still access paths after account deletion or deactivation.

03 Quality Evaluation and Failure Classification

Fix a set of samples including normal, ambiguous, missing information, unauthorized access, and interface failure. Record version, input, output, basis, and human judgment; separately list cases where the system cannot answer but definitely responds as risks.

04 Exception Handling and Release Rollback

Confirm behavior during timeouts, duplicate requests, approval rejections, and dependent service interruptions. Arrange a designated human lead, and rehearse paths for disabling automated execution, rolling back versions, and restoring work.

05 Cost, Capacity, and Long-term Ownership

Observe the invocation, latency, and resource cost of complete tasks, not just the cost of a single model call. Agree on logging and knowledge maintenance, alerting responsibilities, support windows, and a regular review cadence.

Compile the inspection results into a release note

A usable release note must be understandable by people not in the development group: why it went live, what went live, and what to do if it fails.

Release scope

List the version, user scope, open tasks, data and tools, and explicitly unsupported capabilities. Record the differences between this version and the previous one.

Acceptance evidence

Include associated evaluation samples, permission tests, device or performance records, and unresolved issues. Known issues must state the impact, workaround, and whether they block release.

Decisions and responsibilities

Record the business and technical owners, confirmation time, monitoring items, manual takeover and rollback owners, and conditions for the next review.

Example: customer service order inquiry

This is a hypothetical scenario to illustrate the method, not a completed client project.

Prototype stage

After entering the order number, it can display the logistics summary, only showing that the model and query chain work. It does not yet prove permission isolation between different customers, duplicate requests, or exception handling.

Complete before production

Verify that the user is indeed authorized to query that order; logistics API timeouts must not generate false statuses; minimize the return of sensitive fields; show the data retrieval time for key results and provide a channel for human handoff.

Whether to release

Validate with agreed normal and exception samples, and keep failure records. Only when permissions, facts, exceptions, and owners are all clear, open it to users in the confirmed scope, and continue to observe.

Lixing, FDE Pre-Deployment Engineering

Which release condition is your pilot stuck on?

Describe what tasks can currently be completed, which systems are connected, and which issues remain unverifiable.