Jason Fleagle

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.

A Gemini 4 Carbon trial should keep time limits fixed. If one agent gets ten hours and another gets six, the score mixes capability with extra runway. Set the deadline your team can actually afford, then compare how much finished, accepted work arrives before it.

Gemini 4 Carbon raises a useful security question: can the fix survive the exploit without breaking legitimate use? Add both tests to the same review. An exploit that stops working because the feature was disabled is not the repair your customer actually asked for.

Happy Birthday to my wonderful wife Katie Fleagle. Love you and looking forward to another journey around the sun together.

For a Gemini 4 Carbon pilot, ask the reviewer to judge patches without knowing which model wrote them. A leaderboard lead can bias acceptance before the code is tested. Keep the model labels hidden until the same tests and review criteria have been applied.

For a Muse Wonder pilot, include abandoned runs in the cost per accepted result. A cheap attempt that needs three restarts can cost more than a slower model that finishes. Compare the full assignment, including review time, before choosing which model to deploy.

For a Muse Wonder pilot, give the agent a signed contract and a newer sales note with conflicting delivery dates. Ask it to draft the customer update. Newer does not mean authoritative. It should surface the conflict before turning a casual note into a promise.

I’m in Vancouver this week for Cisco Partner Summit. Looking forward to great meetings, conversations, and collaborating with Cisco Leaders, teams, and partners. It’s going to be a great week with our NETSYNC team. Message me if you’d like to connect while we’re in town.

Cancel a Muse Wonder pilot assignment while a tool request is still running. When the result arrives, check whether the agent resumes the cancelled work. A stop button needs to stop follow-up actions too, or a delayed response can revive a job you already ended.

An AI morning brief can look normal after its customer-data connection fails. In a pilot, disconnect that source and check the next brief. It should show the last successful refresh and the missing coverage before anyone relies on its list of urgent cases.

A model that runs on one GPU server may still need two when maintenance starts. Before buying hardware for private AI, test whether the service stays usable with a node offline. Size the deployment for recovery and peak demand, not the best single-user demo.

An AI vendor can delete a prompt while your debugging service keeps the full conversation. Put application logs in the security review before approving sensitive data. Test deletion across the whole request path, including copies your own team chose to retain.

An AI pilot can save the analyst an hour and cost the manager two in review. Put both people in the evaluation. Count time spent checking and correcting the output, not just time to generate it. A faster draft is a poor bargain if it moves work up the pay scale.

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.