When I was a forward-deployed PM for AI agents, most of my time went to healthcare and life sciences customers, like TailorCare. Everyone wanted to see the conversation in the demo. But what decided whether a program worked was what happened after the agent stopped talking.
Last week I wrote about teaching your AI agent when to stop talking. This is the other half: once the agent stops, who picks up, and what do they get? My short version is that healthcare AI agents fail at the handoff, not the conversation.
The handoff is a product, not a fallback
Most teams treat escalation as an error path. “If the agent can’t handle it, transfer.” Then they find out the transfer goes to a queue nobody staffs at 6pm, or lands on a nurse with no context, or drops the patient into a phone tree.
A handoff needs the same design attention as the agent itself:
- Where does it go? A warm transfer, a scheduled callback, a task in a care team’s queue? These are different promises to the patient.
- What context travels with it? The person picking up shouldn’t have to ask the patient to start over.
- Who owns it, and when? Hours, coverage, and response expectations need an owner on the customer’s side, by name.
- What does the agent tell the patient? “Someone will call you back” is a commitment. Only say it if it’s true.
When a handoff is designed well, patients barely notice it. When it isn’t, it’s the one part of the experience they remember.
Escalation is an ops contract, not a prompt
This is where the forward-deployed work really lives. You can’t prompt your way into a staffed callback queue. Making the handoff real means sitting with the customer’s operations team and working out who catches what, in which system, and how fast. It’s unglamorous and it’s the job.
It’s also why I think regulated verticals are a moat for the teams willing to do this work. The model is available to everyone. Fitting agent handoffs into a care team’s real workflow, with clinical and compliance signed off, is not.
Measure the handoffs, not just the calls
Teams usually report on what the agent did: calls completed, conversations finished, appointments booked. I’d add a second set of measures focused on the handoff:
- How often does the agent hand off, and for which reasons?
- How long until a human actually engages?
- What happens to the patient after the handoff?
- Did the receiving person have what they needed, or did they start over?
A rising handoff rate isn’t automatically bad. Sometimes it means the agent is getting better at recognizing its limits. What matters is whether each handoff lands somewhere real.
The same pattern shows up in internal AI
I build internal AI now, and the lesson carries over almost word for word. Internal agents earn trust by passing work to a person with enough context that nothing gets lost, and by having someone own what happens next. Capability gets you the demo. Handoffs get you adoption.
If you’re building agents in a regulated space, treat the handoff like a feature someone owns. That’s usually the difference between a pilot and a program.
