Build modular agents on one shared health context.
Health Core is longitudinal infrastructure, not a feature bag. Agents read and write a shared, permissioned, provenance-first profile — instead of becoming isolated bots with separate truths.
One profile beats a dozen disconnected bots.
Isolated chatbots and single-purpose mini-apps each build their own version of you. Specialist agents over a shared Longitudinal Health Profile avoid separate truths — every interpretation lives in one coherent, inspectable model.
The broken pattern
Each app stores its own slice. Each chatbot forgets yesterday. Contradictions hide in silos — and the user becomes the integration layer.
The Health Core pattern
Specialist agents share one LHP. Writes carry provenance and confidence. Permissions scope what each agent can see and do. Coherence is structural, not hoped for.
Stable building blocks, not ad-hoc features.
Health Core is infrastructure. These sixteen primitives are the vocabulary every agent, surface, and integration maps to.
Every capability maps back to these primitives or deliberately extends them. See the LHP model →
Specialists that share context, not compete for it.
The user sees a unified companion. Underneath, modular agents operate over one permissioned profile — each with declared scope and accountability.
Unified companion, many specialists
Users experience one coherent companion — but underneath, specialist agents handle sleep, recovery, nutrition, and more without fragmenting the model.
Each agent has scope, permissions, provenance, accountability
Every agent declares what it can read, what it can write, and what it inferred. Outputs carry source, confidence, and audit trail — never anonymous black-box assertions.
Agents reason over the shared LHP
Specialists don't maintain private silos. They read and write the same Longitudinal Health Profile, so interpretations stay coherent across domains and over time.
New agents plug in safely
Add a specialist without rewriting the core. Permissions, provenance, and the autonomy ladder gate what each agent can do — so expansion doesn't erode safety or user agency.
Example specialist agents — all reading and writing the same LHP:
- Sleep
- Recovery
- Exercise
- Nutrition
- Mental state
- Medication
- Symptom
- Experiment
- Clinical prep
- Preventive
- Care coordination
Autonomy earned through checkpoints.
Agents don't jump to action. The ladder defines what each capability level requires — and higher rungs demand more safety, consent, auditability, and confidence to justify them.
Explain
Summarise and clarify what the profile already contains — grounded in the user's own data.Interpret
Surface patterns, inconsistencies, gaps, and possible relationships across the longitudinal record.Suggest
Propose next steps, questions, experiments, or behavioural changes for the user to consider.Plan
Build structured, cross-domain plans — sleep, training, nutrition, recovery, and care preparation.Follow up
Remember unresolved issues, check whether plans were followed, and adapt as new data arrives.Coordinate
Hand context between agents and domains while preserving coherence in the shared profile.Act with consent
Trigger reminders, update records, or prepare exports — only within explicitly approved permissions.Support care continuity
Bridge daily life, personal optimisation, and provider-facing workflows without collapsing them into one undifferentiated stream.
Infrastructure principles, not marketing features.
These constraints shape every schema decision, agent contract, and surface in Health Core.
Longitudinal by default
Every primitive is designed for trajectories, not snapshots. Events accumulate meaning as the profile evolves.
Provenance-first
Observations carry where they came from. Agents and interfaces must show lineage — not just conclusions.
Timeline-aware
Context depends on when things happened and what came before. Ordering and duration matter.
Uncertainty-aware
Confidence is a first-class field. The system never presents inference as fact when it isn't justified.
User-correctable
People can inspect, approve, correct, and restrict what the model holds. The profile belongs to the user.
Privacy-preserving
Scoped permissions and consent checkpoints gate access. Higher autonomy requires stronger justification.
Open-core compatible
Stable primitives and inspectable schema behaviour — designed so the core can be extended, audited, and shared.
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We're onboarding engineers and agent builders who want health AI grounded in shared context, provenance, and consent — not another isolated chatbot.
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