Production AI System Checklist: Security, Permissions, Evals, and Monitoring

Shipping a production AI system is different from showing a prototype. A prototype proves that a model can do something interesting. A production system proves that a workflow can run reliably with real users, real data, permissions, monitoring, and support.

Use this checklist before launch.

Scope and workflow

The system should have a defined job. It should be clear who uses it, what input it accepts, what output it produces, and what happens next. If the system is an agent, list the tools it can use and the actions it cannot take.

Good scope statements are narrow enough to test. "Help the support team draft replies for billing questions" is better than "automate support."

Data sources

List every source the system can access. For each source, define owner, freshness, sensitivity, permission model, and whether it can be used in model prompts. If retrieval is involved, document chunking, ranking, metadata, and update cadence.

The system should not rely on mystery knowledge. Users and operators should know where answers come from.

Permissions and identity

Production AI should follow the same access rules as the product or workflow around it. A user should not receive an answer based on documents they cannot access. An agent should not perform actions outside its role.

Check authentication, authorization, service accounts, audit logs, secret storage, and tool permissions. For internal systems, role-based access often matters more than model choice.

Evaluation

Create an evaluation set before launch. Include normal cases, edge cases, negative cases, and permission tests. Define what a correct answer looks like and how failures will be categorized.

For RAG systems, evaluate retrieval and generation separately. For automation systems, evaluate task completion, review acceptance, and failure handling. For coding tools, evaluate compile success, test pass rate, and reviewer acceptance.

Human review and escalation

Decide which actions require human approval. For many workflows, AI should draft, summarize, classify, or recommend while a human approves the final action. Higher-risk workflows need stronger gates.

Escalation rules should be visible in the product and enforced by the system where possible. Do not rely entirely on a model to remember safety policy.

Monitoring and logs

Operators need to see what happened. Log inputs, outputs, retrieved sources, tool calls, errors, latency, cost, user feedback, and review decisions. Sensitive fields should be masked according to policy.

Monitoring should answer practical questions: Is the system being used? Is quality improving? Are costs within range? Which failures repeat? Which source caused a bad answer?

Cost and latency

Production AI has operating costs. Estimate cost per task, expected monthly volume, peak load, model usage, retrieval cost, storage, and review time. Also set latency expectations. Some workflows can wait 30 seconds. Others need responses in under two seconds.

Model selection should reflect the workflow, not a leaderboard alone.

Ownership

Every production AI system needs owners. Who updates sources? Who reviews failures? Who approves prompt changes? Who monitors cost? Who handles incidents? Who decides when the system can expand scope?

Without ownership, quality decays as documents, users, and business rules change.

Launch gate

Before release, confirm:

• The workflow and scope are documented.
• Data sources and permissions are approved.
• Evaluation passes agreed thresholds.
• Human review and escalation paths work.
• Monitoring and logs are available.
• Cost and latency are acceptable.
• An owner is assigned for ongoing improvement.

When these pieces are in place, AI can move from experiment to infrastructure.

Inferencia designs production AI systems with security, evaluation, monitoring, and workflow ownership built in from the start. Review our process or start a project.