AI Agent Implementation Checklist for Business Operations

Define the job before choosing the agent

Write the trigger, inputs, expected output, completed outcome, and named owner. List actions the agent may take and actions it must never take. Choose one recurring task with enough value to measure and a visible stopping point. A description such as help the sales team is not an implementation boundary; prepare a draft handoff from an approved opportunity is.

Inventory systems, data, and permissions

For every connected tool, record the account plan, authentication method, permitted records, allowed actions, rate limits, and source of truth. Give the agent the narrowest service permissions that support the task. Keep read, propose, approve, and execute permissions separate so a useful reasoning capability does not automatically receive broad operational authority.

Design human approval and exceptions

Name the actions that require approval, the reviewer, the evidence they see, and the fallback when that person is unavailable. Define what happens when information is missing, systems disagree, a tool call fails, or a case falls outside policy. The review queue must preserve context and make the next action clear instead of leaving people to reconstruct the case from logs.

Build a representative evaluation set

Use real but appropriately protected examples of ordinary, incomplete, ambiguous, and high-impact cases. Define task-specific pass criteria before testing. Measure the correctness of the completed business outcome, required corrections, policy violations, and escalation quality. A fluent answer is not sufficient when the agent is expected to update a CRM, send a message, or change a record.

Prepare production operation

Assign a business owner and technical owner. Provide visible status, action history, alerts, retry rules, volume limits, and a way to pause execution. Document model, prompt, tool, policy, and workflow changes that could alter behavior. Agree how incidents, user feedback, and failed evaluations affect authority or release decisions.

Launch with a reversible pilot

Begin with limited users, volume, and authority. Compare the pilot with the existing baseline for completed work, handling time, corrections, review effort, exceptions, and cost. Expand only when the operating evidence supports it. Keep a tested manual path or rollback for work that cannot wait while the agent is paused or corrected.

Primary references

The guidance above is AgenticShip's proposed approach. These references provide technical background for the relevant recommendations.

• NIST AI Risk Management Framework Core
• NIST Generative AI Profile