Written by Meredith Schulz - Lena Health Product Manager
Launch day is not the finish line.
It's when your AI agent meets real-world calls, patient variability, and unpredictable environments. The best teams treat this as the start of a continuous optimization cycle, not a one-time milestone.
The first agent prompt and conversation design is a hypothesis. The more grounded the hypothesis is in real patient conversations that your human staff have already handled, the better the starting point. Use those transcripts to capture realistic test cases and ensure you're designing for more than the easiest scenarios.
Live calls will reveal situations that testing cannot fully replicate. Background noise, caregiver answers, voicemail quirks, and interruptions will all influence call flow. Your design should anticipate these scenarios with recovery points, flexible prompts, and clear handoff protocols for urgent or sensitive situations.
Avoid Overbuilding Before You Have Real Call Data
The biggest early risk is assuming you know every conversational scenario in advance. Hypothetical cases can waste time and dilute focus. Start with a tight, high-probability set of scenarios and let real data expand it.
A practical starting point is about 20 test case scenarios. These can include interruption handling, someone else answering the phone, background noise from a busy environment, a voicemail system with unexpected prompts, a patient joining the call mid-sentence, a call dropping and reconnecting, or a caregiver speaking on the patient's behalf. Building around these real-world situations ensures the agent is ready for the conditions it will face from day one.
When you expand, do it in response to patterns observed across many calls, not isolated incidents. This ensures every new branch in the workflow tree solves a real problem, not an imagined one.
Define and Track the Right Metrics
Optimization requires a clear definition of success and consistent measurement. Without this, you won't know if your changes are working or where to focus.
Track:
Goal completion: percentage of patients who complete the intended action
Escalation rates: frequency and cause of transfers to staff
Callback backlog: volume and urgency level of follow-up requests
Abandonment rate: with breakdown by technical vs conversational cause
Technical performance: latency in speech-to-text or backend systems, function call success rates
These numbers should guide daily decisions. A spike in escalations may mean the agent is getting stuck in a workflow. A drop in completion rate could point to confusing prompts or unclear next steps.
Keep the Technology Invisible
The best patient experiences happen when the technology disappears into the background. A fast, reliable technical foundation gives every design a better chance to succeed. Even the most thoughtful conversation design will fail if the underlying systems are slow or unreliable.
Failures in function calls, dropped connections, or poor audio quality can erode trust in a single interaction. Testing should include a variety of real-world conditions, for example, different phone types, carriers, and noisy environments. These tests analyze how the agent performs under less-than-ideal circumstances.
Integration reliability matters as much as conversation design. Function calls need to trigger at the right time and return accurate data to keep the interaction on track. A missed eligibility check, for example, can block an appointment from being booked.
Fix these technical issues before revisiting the script. Solid infrastructure ensures every workflow starts from a position of strength, giving the agent the best chance to deliver a seamless patient experience.
Balance Speed with Stability
Some problems call for immediate fixes. Others require measured updates backed by sufficient data.
Apply urgent fixes right away, then retest both conversation flow and technical performance
Use A/B testing for script or logic changes before committing
Gather 50–100 calls’ worth of data before making large-scale updates, so you act on patterns rather than outliers
Test all related workflows after any integration or function change to prevent breaking dependent processes
This approach addresses urgent issues promptly while keeping the system stable enough for reliable, human-guided improvement.
Commit to Continuous Human-Guided Optimization
While AI agents can later be taught to improve themselves, the proper foundation must be built with structured human review and deliberate iteration.
Review transcripts, tool call logs, and prompt performance together for a full view of how the agent is operating. Update guardrails and decision logic as program goals or patient behaviors evolve. Add new functions only when they clearly improve outcomes or efficiency.
Maintain a human-in-the-loop approach for situations that require empathy, safety, or complex reasoning. These moments protect both patient trust and clinical safety.
The Real Goal
The organizations that succeed are not the ones that launch perfectly on day one.
They are the ones that build a system for continuous improvement, where every call feeds into a smarter, more reliable program. In healthcare, this means focusing on the right scope, as roughly 95% of patient needs are non-clinical.
These are exactly the kinds of workflows that voice AI can manage at scale, freeing human teams to focus on the smaller percentage of interactions that require true clinical judgment.
The first launch is not the end of the work, but rather the foundation for everything that comes after.