Use case · Healthcare

Medical Device Governance

Govern AI agents around medical devices with patterns validated against documented device-safety incidents.

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The risk

What an ungoverned agent gets wrong here.

Software as a Medical Device (SaMD, software that performs a clinical function on its own) makes decisions at the scale of whole patient populations. Every device platform governs the algorithm's output (its sensitivity, specificity, and accuracy at release) but not its behavior over time: the diagnostic drift, the population it was never validated on, the dose outside the safe range, the model quietly swapped mid-deployment. That behavioral gap is where a cleared algorithm becomes a patient-safety risk. It is a documented pattern (a widely deployed sepsis-prediction model that independent validation found missed roughly two-thirds of cases, and large device recalls tied to hundreds of reported deaths), and without governance there is no drift detection, no enforced boundary, and no tamper-evident record a regulator will accept.

Without governance

Where it goes wrong.

01

A model that drifts off its baseline

A diagnostic algorithm slowly deviates from the behavior it was validated on. The degradation is gradual enough that no dashboard flags it, and a model that was safe at clearance is now missing findings it used to catch.

02

The population it was never tested on

An algorithm validated on one demographic cohort is run on patients well outside it (different ages, comorbidities, or presentations), and its real-world accuracy quietly falls below what the validation cohort suggested.

03

A dose outside the safe range

A model recommends or controls a dosage beyond the validated therapeutic range. Nothing enforces a hard limit, so an over-delivery or a contraindicated combination can reach the patient.

04

A model swapped without approval

A clinical model is replaced or retrained mid-deployment without passing through the predetermined change control plan, so the version running on patients is not the version anyone authorized.

With Agentomy

How Agentomy governs it.

01

Discover every agent touching a device

Inventory every agent and model with reach into clinical device software. An unregistered agent falls back to read-only: it can observe but cannot drive a clinical decision until it is authorized.

02

Enforce clinical boundaries

Dose ranges and validated-population tolerances are held on the server, never read from the request being judged. A decision that reports a dosage is checked against the therapeutic range registered for that device and drug; one that reports population drift is checked against the registered clinical-evaluation tolerance. A range sent alongside the reading is dropped and recorded as an attempt, and a device with nothing registered is denied rather than waved through. The model hash is compared against the PCCP-authorized baseline, so an unapproved swap is denied and quarantined rather than shipped.

03

Detect drift and halt fast

Per-model behavioral baselines flag diagnostic drift and population shift with auto-quarantine, and one switch halts every governed device algorithm when a model starts behaving unsafely.

04

Prove it to a regulator

A tamper-evident, hash-linked log captures every clinical decision and model change (what ran, on which patient population, under whose authorization, and why), exportable as post-market surveillance evidence.

What it detects

The detection patterns behind this workload.

Agentomy carries a dedicated MED pattern family for Medical Device Governance. Each one is a specific failure this layer watches for, with the signal it watches, checked at runtime before the action reaches your systems.

10 of 166 governance patterns
  • Critical
  • High
  • MED-001Severity: Critical

    Algorithm drift

    DetectionContinuous performance metric comparison against PCCP-validated baseline.

  • MED-003Severity: Critical

    Dosage boundary violation

    DetectionReal-time boundary checking on all dosage computation outputs.

  • MED-004Severity: Critical

    Unauthorized model update

    DetectionModel version and weight hash tracking.

  • MED-007Severity: Critical

    Consent boundary violation

    DetectionConsent scope tracking per patient record.

  • MED-009Severity: Critical

    Recall response failure

    DetectionRecall response tracking.

  • MED-002Severity: High

    Population shift

    DetectionStatistical distribution comparison between incoming patient demographics and validated training population.

  • MED-005Severity: High

    Audit gap

    DetectionContinuous audit chain integrity verification.

  • MED-006Severity: High

    Alert fatigue

    DetectionTrack alert-to-acknowledgment ratios and dismissal patterns.

  • MED-008Severity: High

    Interoperability failure

    DetectionMonitor clinical data interface health.

  • MED-010Severity: High

    Validation gap

    DetectionValidation evidence registry tracking.

MED family, MED-001 to MED-010 · 10 of 166 governance patterns Agentomy enforces at runtime. Severity as classified in the pattern definition.

Frameworks

Maps to what you answer to.

Agentomy does not certify you. It gives you the enforcement and the audit trail these frameworks ask for, so readiness is something you can show rather than assert.

FDA PCCPthe US Food and Drug Administration's Predetermined Change Control Plan guidance, which lets an AI-enabled device update within pre-authorized bounds; version tracking plus authorization gives you evidence a change stayed inside the planEU MDRthe European Union Medical Device Regulation (2017/745) governing device software and its post-market surveillance; the audit trail provides the continuous-monitoring record it expectsEU AI Actthe European Union's risk-based AI law, which classifies medical-device AI as high-risk with risk-management, transparency, and human-oversight duties; Agentomy gives you the enforcement and evidence to operationalize them21 CFR Part 11the FDA rule (Title 21 of the Code of Federal Regulations, Part 11) for trustworthy electronic records and signatures; the hash-linked, attributable trail provides the record integrity it requires
See it in the record

Every action, logged and provable.

A tamper-evident, hash-linked trail of every governance decision for this workload: what an agent did, under whose authorization, and why. Plain-English reasons for every allow and deny, exportable to the framework your auditors care about.

Agentomy Command CenterFilter Medical Device Governance
  • 100% Integrity
  • 43,460 Blocks
  • SHA-256 hash-linked
Audit trail: one tamper-evident block per governance decision, hash-linked to the one before it
BlockTimestampAgentActionTierHash
43,460Today 12:42:08clinical-research-agentdata_access_requestEvaluator045757d57d32dff1
43,459Today 12:41:54patient-intake-agentoutput_validationAnalystba02f6226d5a4a14
43,458Today 12:41:37supplier-review-agentpolicy_checkBuilder3b00130cbf6d53ab
43,457Today 12:40:58claims-extract-agentbehavior_driftOperator2bd643324d204d61
43,456Today 12:40:21consent-draft-agenthalt_initiatedStrategist952f1247295591d0
43,455Today 12:39:46care-report-agentprompt_reviewEvaluator23a1c1837d692e5f
Governance events today: 12,842Demo environment
Illustrative interface with sample data, in the shipped Command Center’s structure. Not a customer environment and not a live feed.

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