Delivery · the operating agreement
Managed AI delivery, ownership and human approval.
The tools we connect. The decisions you keep. How we handle mistakes and changes. Agree the operating scope before the first workflow goes live.
AI workflow integration with existing tools
Start with the tools and files the team already uses. Confirm each connection with a real sample before it becomes part of the workflow.
Source context for AI agent work
Keep the source material, examples, client constraints and decision history close to the work. A draft without its context is hard to review.
Human approval of sensitive AI agent actions
External messages, commitments, access changes and other sensitive actions have a named human approver. The workflow prepares; the owner decides.
AI output quality and review
Agree what a useful output looks like, review early examples and keep corrections visible. Quality is a practice the team can repeat.
AI workflow failures and escalation
When a source is missing, a rule conflicts or a draft is unsafe, the workflow stops at that boundary and tells the operator what needs attention.
AI workflow performance monitoring
Track the signals that matter to the work: reviewed outputs, accepted actions, exceptions and time saved. Choose measures the owner can inspect.
AI workflow ownership and data retention
The firm knows who owns the workflow, which sources it uses and how long its records should remain. Retention follows the firm’s policy and the work’s needs.
AI workflow maintenance and handover
Rules, sources and integrations change. Maintain & Train keeps them current, and the handover leaves the firm with the decisions needed to operate, change or retire the workflow.
Demonstrate the AI workflow
The workflow runs on a real case, with its sources, rules and stopping points visible.
Review AI workflow outputs
The team corrects early outputs and confirms who approves which action.
Maintain the AI workflow
Corrections become clearer rules and a review rhythm the team can sustain.