CARC — Clinical Agent Runtime Control
Runtime control
for clinical AI agents.
CARC gives healthcare AI companies an independent runtime control layer for governing, reviewing, and reconstructing clinical agent behaviour before outputs enter clinical workflows.
Keep your clinical AI. Add runtime control. Send a question first.
01 — Runtime Architecture
A control layer between your agent and the clinical workflow.
CARC does not replace your clinical AI models or agents. It runs alongside them, in the path between what your agent decides and what happens next in the clinical workflow.
Every recommendation, escalation, deferral, and override passes through CARC's runtime engines, where it becomes observable, governable, and reconstructable.
Adapter Registry
Connects CARC to your agent's existing interfaces without requiring a rebuild.
Runtime Engine
Observes agent behaviour as it happens and routes it through policy and risk evaluation.
Evidence Engine
Captures decision context, clinician response, and outcome into a structured, reconstructable record.
Control Console
Where runtime sessions, agent runs, and human review come together for your team.
Agent
Registry
Engine
Engine
Console
02 — Core Capabilities
Runtime control is
the missing layer.
Healthcare AI companies need to govern behaviour, not just documentation. CARC makes agent behaviour visible, owned, interruptible, and reconstructable while your product is operating in real workflows.
CARC's control layer sits between model approval and operational evidence. It shows when the agent acted, why it acted, what context it used, and who intervened.
Agent Behaviour
Know what the agent recommended, when confidence changed, when it escalated or deferred, and whether similar cases produce consistent behaviour.
Clinical Context
Capture the clinical context, workflow state, model version, thresholds, alerts, suppression rules, and intervention options that shaped the agent's action.
Human Intervention
Track who accepted, modified, overrode, escalated, or rejected the agent's recommendation, with evidence that can survive audit and incident review.
Can you reconstruct what the clinical agent did, why it acted, what context it used, how the clinician responded, and whether the same case would behave the same way again?
03 — Integration Models
Keep your clinical AI. Add runtime control.
CARC integrates alongside the clinical AI you already have. Choose the integration pattern that fits your architecture — no model rebuild required.
SDK-Embedded
Instrument your agent directly with the CARC Runtime SDK, wrapping recommendations, escalations, and overrides as they happen.
Adapter / Gateway
Route agent traffic through a CARC adapter sitting in front of your existing agent, with no changes to the agent's own code path.
Event Ingestion
Stream existing decision and intervention events into CARC's Runtime Engine for evidence capture and reconstruction without live instrumentation.
04 — Developer Experience
Built for engineering teams, not just compliance teams.
Design Partner Preview
The CARC Developer Platform, Runtime API, and Control Console are implemented today and available to design partners while hosted public access rolls out.
Illustrative example
05 — Runtime Metrics
Operational evidence for every clinical agent decision.
Runtime Metrics turns raw agent activity into evidence: what happened, who was involved, and whether it can be reconstructed later. Interface shown below is illustrative, not live customer data.
Sample interface data for illustration only
06 — Clinical Use Cases
Built for healthcare AI companies moving into agentic workflows.
For teams where clinical AI agents are entering deployment, audit, post-market monitoring, or regulated clinical use — and where behaviour after deployment must be controlled, not inferred.
Healthcare AI Companies
Clinical AI products, SaMD, diagnostic support, imaging AI, triage agents, clinical workflow agents, and platforms preparing for deployment into real care pathways.
Product & Platform Teams
CTOs, Heads of AI, product leaders, and clinical AI platform teams who need runtime monitoring, escalation logic, override evidence, and reliable decision reconstruction.
Regulatory & Quality Leaders
Teams preparing for EU AI Act, MDR, clinical deployment, audit, conformity assessment, human oversight evidence, or post-market AI monitoring.
07 — Platform Principles
Runtime oversight for regulated healthcare AI.
CARC is built around the evidence healthcare AI companies need when oversight, intervention, auditability, and post-market monitoring move from policy language into deployed product behaviour.
Article 14
Effective Human Oversight
Human oversight AI evidence must show more than a named reviewer. CARC's Decision Controller defines when a clinician can intervene, what they saw, how the agent behaved, and whether oversight was operational at the point of use.
GSPR 14.2(d)
Intervention and Override Evidence
MDR AI software needs clear evidence of user intervention, escalation, and override paths where clinical decisions are influenced by automated processes. CARC's Evidence Engine makes those paths explicit and reconstructable.
Surveillance
Behaviour Monitoring After Deployment
Post-market AI monitoring depends on knowing how the agent behaves across real cases: recommendations, confidence shifts, escalations, deferrals, overrides, clinician actions, and repeated-case consistency. CARC's Runtime Metrics engine is built for exactly this.
08 — Company
Giggle AI Innovation builds runtime infrastructure for clinical AI systems. CARC is our flagship product — the runtime control layer for clinical AI agents.
I work directly with design partners integrating CARC into their clinical AI systems. If your product is moving into deployment, audit, or post-market monitoring, request a demo.
— Thokozile Phiri, Founder, Giggle AI InnovationQuestions
What is CARC?
CARC (Clinical Agent Runtime Control) is a runtime control layer for clinical AI agents. It sits between your clinical AI and the workflow it acts in, making agent behaviour observable, governable, interruptible, and reconstructable in production.
Is CARC live today?
CARC's Developer Platform, API, and Control Console are implemented and available to design partners under private preview. Public hosted access is rolling out — request a demo to get access now.
Does CARC replace our existing clinical AI?
No. CARC does not replace your clinical AI models or agents. It adds a runtime control layer alongside what you already have — keep your clinical AI, add runtime control.
Is this a SaaS platform or an advisory engagement?
CARC is a software platform: a Runtime Engine, Decision Controller, Evidence Engine, Policy Engine, Risk Engine, and Control Console with a Developer SDK and API. Giggle AI Innovation also works directly with design partners during private preview to integrate CARC into their systems.
Who is CARC for?
Healthcare AI companies, CTOs, Heads of AI, product and platform engineering teams, and regulatory or quality leaders preparing clinical AI agents for deployment, audit, or post-market monitoring.
Request Demo
Know how your
clinical agent behaves
after deployment.
Request a demo to see how CARC governs, reviews, and reconstructs clinical agent behaviour in production — and how to join as a design partner today.
No preparation required. No commitment beyond the conversation.