Generative AI supports the workflow. The deterministic engine supports interpretation. The licensed clinician makes the clinical decision. The part that must be correct and auditable is not left to a probabilistic model — “determinism first, generative second.”
Why agents exist
Preventive medicine creates operational complexity. A patient may need to complete intake, upload labs, confirm fasting status, report medications, clarify symptoms, schedule review, receive follow-up, repeat tests, and understand next steps. A clinician may need to review many inputs before deciding whether a result is meaningful. Agents help with intake, scheduling, record consolidation, missing-data detection, review preparation, follow-up, patient communication, and operator support. They reduce administrative load. They do not replace the clinical logic or the clinician.The core architectural separation
The system has three distinct layers, which prevents a common failure mode in clinical AI: allowing a fluent model response to become the medical decision.
The engine may produce a structured output. The agent may organize or explain it. The clinician decides what it means clinically.
Current and proposed agent roster
The final whitepaper should clearly distinguish live agents from roadmap agents so nothing reads as operational if it is not yet live.
The Coordinator is the clearest live or near-live agent. It manages intake, scheduling, follow-up, record consolidation, and advisory flagging against treatment-eligibility rules. It is always transparent as an agent and never prescribes or decides.
The Coordinator agent
The Coordinator is the orchestration agent. Its job is to move the patient journey forward without crossing into clinical decision-making: intake management, lab workflow, context collection (fasting status, pregnancy status, contraindication screen), scheduling, record consolidation, advisory flagging, and follow-up.What agents are and are not allowed to do
Agents are allowed to
Ask for missing intake facts, explain what information is still needed, summarize uploaded records, surface engine states to clinicians, route ambiguous cases to review, draft patient-facing next steps from approved language, remind patients about follow-up, and help operators manage workflow.
Agents are not allowed to
Diagnose, prescribe, authorize treatment, override a physician, override deterministic engine logic, invent a biomarker pattern, apply unapproved calibrations, hide uncertainty, present themselves as human, or show clinician-only conclusions to patients.
Tool-use and grounding
Agents operate through tool-use, not free clinical generation. An agent retrieves structured data, queries the engine, reads decision traces, identifies missing facts, or surfaces approved language. It does not create new medical logic from its own model output.Agent interaction with review states
Agents must respect engine review states.
The agent’s role is especially valuable when data is incomplete. Instead of producing a weak interpretation, it can ask for missing facts — for example, “We need your fasting status before this metabolic marker can be interpreted safely.” That is agent value without clinical overreach.
Agent interaction with treatment flags
Treatment workflows require strict boundaries. The GLP-1 flow shows the correct pattern: the engine evaluates fasting gates, candidacy rules, hardening checks, and advisory flag logic; if conditions are met, a clinician-required flag is raised. The patient does not see “eligible” — the language is “possible candidate, requires medical evaluation” — while the clinician sees the contributing rules and evidence trace.Agent interaction with calibrations
Agents may surface calibration context, but they must not create or apply calibrations independently. They may ask for missing context (based on required runtime facts), explain why context may matter (approved language), surface the calibration trace to a clinician, and suggest confirmatory testing only if the engine or approved protocol supports it. Agents may not apply calibration from model judgment, infer ancestry or genotype from appearance, or override genotype or clinician input. Sensitive context remains governed by the calibration layer and Medical Director review.Agent memory and longitudinal follow-up
A preventive system depends on time. Agents can help track repeat testing, missing labs, trend review, medication follow-up, lifestyle follow-up, safety monitoring, and retesting after suppression resolves. The Follow-up / Longitudinal agent should not interpret trends freely; it surfaces deterministic engine outputs, missing facts, or protocol-defined follow-up needs.Transparency
Patient-facing
Patients should know when they are interacting with an agent rather than a human clinician. Safe: “I’m the Consensus Coordinator. I help collect information, organize your next steps, and prepare your case for clinical review.” Unsafe: “I’m your doctor.”
Clinician-facing
When an agent summarizes a case, the clinician should see source documents, engine outputs, decision IDs, missing facts, calibrations, safety flags, treatment flags, completeness, and a clear distinction between agent-generated text and source data.
Model-provider governance
Model-provider governance is still a completion item and should be finalized with engineering before the final whitepaper is shared externally. The final governance model should include the model provider (name, version, deployment region, contract status), model versioning, change management before switching models, data handling, PHI protection, logging of agent actions and tool calls, pre-deployment evaluation, human sampling of outputs, low-confidence escalation to human review, and incident response to disable or constrain agent behavior.Monitoring of agent outputs
Agent monitoring focuses on safety, accuracy, and boundary preservation — not only whether the agent is helpful, but whether it stays inside its clinical boundaries.Human-review sampling
Even if agents are constrained, outputs should be sampled. Sampling prioritizes treatment-related messages, calibration-related explanations,ACTION/CRITICAL communications, and complex-case clinician summaries (high); WATCH explanations (medium); and normal/optimal explanations and administrative reminders (lower). This helps identify drift, unclear language, or repeated failure modes before they become systemic.
Agent failure modes
A mature AI architecture does not claim agents never fail. It defines how failures are prevented, detected, and corrected.AI agents in the broader stack
The system is AI-native and configuration-driven: a structured clinical-knowledge schema, deterministic runtime, AI agents, member and operator applications, and integrations for labs, payments, and communications. Clinical logic lives in the reviewed schema, not scattered in application code. Agents are one layer of the system, not the system itself.
The agent layer is powerful because it connects workflows. It is safe because it is constrained by the engine.