# Biological-Age Agents at Thrive AI Health

**AI Researcher · Thrive AI Health (OpenAI Startup Fund–backed) · July–October 2025 · San Francisco**

For four months in 2025 I built part of the modeling and agent layer at Thrive AI Health — turning wearable and biomarker data into a biological-age signal, and wiring LangGraph agents on top to recommend personalized interventions.

- **Biological age:** from wearable + biomarker data
- **LangGraph:** agents recommending interventions
- **iOS + Android:** where the code shipped
- **Jul–Oct 2025:** OpenAI Startup Fund–backed

Most of my work is clinical AI inside a hospital. For four months in 2025, I took the same toolkit somewhere very different: a consumer longevity app.

Thrive AI Health is the OpenAI Startup Fund–backed venture building a personalized AI health coach. I joined as an **AI researcher** from July to October 2025, in San Francisco, and worked on the layer that turns a person's day-to-day health data into something the app can actually act on.

## Biological age from wearables and biomarkers

Chronological age is the number of birthdays you've had. **Biological age** is an estimate of how old your body actually behaves — and unlike the calendar, it can move in response to sleep, activity, and metabolic health. That makes it a useful north star for a coaching app: a single, legible signal a person can watch change.

I built **biological-age prediction models from wearable sensor and biomarker data** — the continuous streams from a wrist device combined with lab-style biomarkers — so the app had a grounded estimate to reason about, rather than a generic risk score.

## From signals to advice: LangGraph agents

A number on its own doesn't change anyone's behavior. The harder problem is turning it into *specific, personalized* guidance. I designed **AI agents with LangGraph** to analyze the wearable data and recommend interventions tailored to the individual, not the average.

1. **Model.** Estimate biological age and the underlying signals from the person's wearable and biomarker data.
2. **Reason.** A LangGraph agent examines those signals in context — what changed, what's trending, what's actionable.
3. **Recommend.** The agent proposes personalized interventions — the concrete next step, not a generic tip.
4. **Trace.** I debugged the agents' interactions with **LangSmith**, tracing each run to see where reasoning went right or wrong.

```
  Wearables + biomarkers  ──(via Junction)──►  data pipeline
           │
           ▼
  Biological-age model   ──►  physiological age + signals
           │
           ▼
  LangGraph agent  ──►  reasons over the signals  ──►  personalized intervention
           │                  (traced in LangSmith)
           ▼
  Delivered in the app   (iOS + Android)
```

*The shape of the work, from raw device data to an in-app recommendation.*

### Getting the data in: Junction

Wearable data is messy and comes from many devices. I **integrated wearable data from Junction** — a health-data aggregation platform that normalizes data from hundreds of devices and lab tests — into the AI analysis pipelines, so the models and agents worked from one clean, consistent stream instead of a dozen bespoke device formats.

### Shipping it: iOS and Android

This wasn't a notebook experiment. I **deployed Python code to the iOS and Android app stores**, so the modeling and agent work ran inside a real mobile product that real people used.

## The throughline

Thrive looks like a departure from my hospital work, but it's the same core skill: **put an agent on top of messy health data and make it do something useful and personal.** The stack — LangGraph for orchestration, LangSmith for observability, careful handling of sensitive health data — is exactly what carries back into clinical settings, where the stakes for privacy and reliability are higher still.

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*This describes my own contribution as an AI researcher at Thrive AI Health; the product is Thrive's. Nothing here is medical advice, and no proprietary details are disclosed. Biological-age estimates are research-grade signals, not clinical diagnoses.*

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- Thrive AI Health: https://www.thriveaihealth.com/
- Back to James: https://james.jcweatherhead.com/
- Full CV: https://james.jcweatherhead.com/resume/
- Both of us: https://jcweatherhead.com/