Most AI work dies in the handoff between “looks good” and “ready to use.” Today I rebuilt an AI workshop around proof objects: • source of truth • facilitation path • named owners • readiness gates • presenter notes • verification receipt Agents need a definition of done.

An AI agent that flags a production outage has not fixed it. Before relying on its alerts, test the handoff: can the on-call person clearly see what failed and take over without repeating the investigation? Measure time to recovery, not just time to notification.

An AI escalation brief can cut alerts while increasing risk. Before expanding the pilot, have the case owner inspect a sample of what the agent left out. Track missed urgent cases separately from false alarms. A quieter inbox is not proof of a safer operation.

For a Muse Wonder pilot, give the agent a delivery date that is approved only if inventory arrives Friday. Then ask it to shorten the proposal. Check whether the condition survives. A cleaner sentence can quietly turn a conditional offer into a firm commitment.

A Muse Wonder memory test: prepare proposals for two fictional customers with very similar names but different discounts. Then switch between them. A correct fact recalled for the wrong customer can leak commercial terms while looking like a well-grounded answer.

Buying access to Muse Wonder is not buying the Muse permission system. Before an API pilot, ask who can deny a tool call outside the model. If the answer is another prompt, your application has advice where the product architecture has an enforcement boundary.

A Muse Wonder supplier comparison should survive a vendor asking for your internal specifications. Test whether the agent can finish with public information or flag the gap without uploading the file. A blocked upload should leave useful work, not a stalled task.

For a Muse Wonder pilot, correct a delivery date after the agent has drafted both a proposal and a follow-up email. Check both outputs. Updating the conversation while leaving an old commitment in a saved draft is a memory failure your customer will still receive.

Before a Mistral Large 4 pilot, put an instruction to export a file inside a synthetic test document. The trial should show that the application blocks the export even if the model follows the instruction. A refusal in one demo cannot prove the tool boundary holds.

A Mistral Large 4 pilot needs a review-time budget. Time the person checking citations and fixing the answer as well as the model. If the output takes longer to verify than doing the task directly, a fast response has bought the team more work. Record both times.

For a Mistral Large 4 pilot, test conflicting document revisions before adding more context. An answer can cite a real policy and still use the superseded version. Require the effective date beside the citation so reviewers can catch a well-sourced wrong answer.

In an OpenAI dots proposal pilot, keep "not tested" separate from "passed." If the test environment is down, the agent can still finish the draft. It cannot establish that the integration works. Put that gap beside the commitment, where the approver will see it.

For a Jev service-desk pilot, score missed urgent tickets separately from ordinary routing errors. A model can improve average accuracy while sending the worst cases to the wrong queue. Set the urgent-case failure limit before deciding how much to automate.

An OpenAI dots pilot can finish more work and still make the team slower. Limit how many revisions it can put in the review queue at once. If drafts arrive faster than people can accept them, the backlog has moved from the agent to your most expensive employees.

For a GPT-6.1 Sol pilot, hide the model name from the person reviewing each revision. Count accepted revisions and review minutes before revealing the price. Otherwise, a cheaper model can get a more forgiving review and look like savings it never actually delivered.

Before assigning proposal follow-up to OpenAI dots, decide when it should stop chasing. A missing customer answer is not permission to infer a requirement. Give the agent a deadline for escalating the gap, or persistent execution can become persistent wasted effort.

Thank you to the New Mexico Hospital Association, everyone who joined us, and our Netsync team for a fantastic event! We were honored to support NMHA and share how we help healthcare organizations put technology and AI to work where they can make a meaningful difference: reducing administrative burden, supporting staff, protecting sensitive information, and creating more time for patient care. During “Healthcare AI Without Regret,” we explored practical ways to adopt AI safely, establish clear accountability, and measure whether it’s actually helping the people doing the real healthcare work. To the attendees, thank you for your time, thoughtful questions, and commitment to the communities you serve. We are really looking forward to talking more soon. And to our NETSYNC team, thank you for the work and care you brought to this event. Grateful for the opportunity to learn together and continue these conversations with healthcare leaders and workers across New Mexico.