A working demo is not yet a production system. This guide is for developers deciding what must be specified, evaluated, and monitored before an AI feature handles real users or consequential data. It is a lifecycle checklist rather than a tutorial directory or a claim that one architecture fits every system.
1. Define the task and boundaries
Describe the user problem, intended inputs and outputs, allowed actions, and actions the system must never take. Identify the human owner for exceptions. A narrow task makes failures easier to detect than a vague goal such as “automate support.”
2. Map data and permissions
Record where inputs come from, whether they contain personal or confidential information, where they are sent, who can access them, and how long they are retained. Use the least privilege needed for each integration. Do not connect a model to write-capable tools until permissions and approval paths are understood.
3. Build an evaluation set before tuning
Collect permitted examples that reflect ordinary inputs, edge cases, ambiguous requests, and known failure modes. Define expected behavior and review rules before comparing model or prompt changes. Separate factual correctness, task completion, latency, cost, and user experience; a single score can hide unacceptable failures.
4. Add human review and fallback
Require a person to approve actions that are irreversible, external, sensitive, or costly. For lower-risk work, provide an escalation route and a way to stop the system. A fallback should preserve the user’s task rather than silently produce an empty or fabricated result.
5. Log enough to diagnose, not more than needed
Capture version, decision path, tool call outcome, and error category where appropriate. Minimize retained prompts and personal data, restrict access, and document retention. Logs should help investigate an incident without becoming a second uncontrolled store of sensitive information.
6. Release in stages
Start with a limited environment and a rollback plan. Define release gates using the evaluation set, security review, accessibility, monitoring, and owner sign-off. Watch for input changes and recurring errors after release. If quality falls below the agreed threshold, pause or revert the feature.
Risk framework and implementation references
The NIST AI Risk Management Framework provides a voluntary structure for identifying and managing AI risks; it is a framework, not a certification. Microsoft’s agent safety guidance discusses controls for agentic systems. Adapt the guidance to your system’s threat model and organization rather than copying settings blindly.
Define stage gates and the evidence each gate needs
Give each stage a written exit decision rather than treating a successful demo as approval for the next stage.
| Stage | Evidence to keep | Do not advance when |
|---|---|---|
| Prototype | Bounded task, permitted sample data, human reference, and known failure | The desired result cannot be checked or the data source is not approved |
| Evaluation | Representative normal, ambiguous, and out-of-scope cases; reviewer and acceptance criteria | High-consequence cases lack a human review or escalation route |
| Integration | Permission map, secret handling, logs, error path, and dependency versions | The system can access or change more than the task requires |
| Limited release | Named owner, monitored cohort, fallback, rollback trigger, and support contact | No one can pause the feature or restore affected data |
Example: for a document summarizer, prototype with public material; evaluate whether each important sentence can be traced to the input; then confirm access control, deletion behavior, and a manual fallback before connecting an internal collection. The example is a planning pattern, not a deployment AILooma has run.
Keep the system’s intended purpose and residual risks beside the release decision. The broader AI implementation roadmap covers organizational ownership and lifecycle planning; this checklist focuses on developer artifacts and release gates.
Related reading
For official learning material and labs, see the developer resource guide. For a worked example of permissions and approval boundaries, read the controlled agent workflow guide.
About the author
Femica Maydinda Harend writes practical technology guides for AILooma focused on useful workflows, responsible controls, and limits that readers can inspect.
Editorial note
This checklist is general information, not a security assessment. Review current primary documentation, organizational requirements, and qualified security advice for production systems.