High-Throughput AWS Deployment and Infrastructure Scaling

To meet the requirement of processing over 100,000 requests per minute (RPM) with low latency, the AgenticPet platform mandates a specialized, high-availability architecture built on the AWS cloud ecosystem, adhering strictly to Well-Architected best practices.

Cloud Architecture Overview for Low-Latency Inference

The core inference engine is deployed using AWS Fargate, a serverless container orchestration service that abstracts away infrastructure management while providing highly scalable and available architecture patterns. This solution is optimized for variable and high-throughput workloads, capable of scaling automatically to support millions of requests per second, far exceeding the 100k+ RPM target. The architecture is simple, scalable, and inherently reliable, designed to operate across multiple Availability Zones (AZs) to ensure high availability and resilience, with automatic failover and seamless task provisioning.

Compute Optimization and Observability

To support the massive compute demands of multimodal inference efficiently, the platform leverages AWS Fargate's optimized task configurations with energy-efficient compute resources that automatically scale based on workload demands while minimizing resource overhead and enhancing sustainability. All machine learning models are standardized and deployed as containerized images managed via Amazon Elastic Container Registry (ECR), facilitating rapid deployment, version control, and seamless rollback capabilities within the Fargate environment. Fargate automatically handles container orchestration, load balancing, and task distribution across multiple Availability Zones, enabling zero-downtime deployments and simplified operational workflows. Operational observability is mandatory for high-throughput environments; Amazon CloudWatch is deployed across the Fargate infrastructure to aggregate logs, create detailed metrics, and provide visualizations via dashboards. This comprehensive monitoring enables real-time visibility into task performance, resource utilization, and endpoint latency, crucial for identifying performance bottlenecks and troubleshooting latency issues during peak request loads, while providing native insights into container behavior and automatic scaling decisions.

Data Persistence and Caching Strategy

Achieving high throughput while maintaining stringent data integrity requires a nuanced, tiered data persistence strategy based on data volatility and regulatory requirements. This necessitates the architectural decision to split data layers between a persistent, regulated storage layer and a high-speed, transient cache layer.

• Persistent Data Storage (VPHI/EHR): This layer is dedicated to regulated, auditable patient records. MongoDB is chosen for its superior scalability through horizontal scaling, sharding, and partitioning, essential for managing vast volumes of clinical data. Furthermore, its built-in support for automatic failover and multi-document ACID transactions ensures data integrity and high availability, essential requirements for clinical record keeping. Deployment leverages MongoDB replica sets running across multiple Availability Zones to maximize availability. For optimal performance, the use of high I/O SSD storage (e.g., AWS I2 instance types) and the implementation of Redundant Array of Independent Disks (RAID) are recommended to enhance durability and throughput.

• Ephemeral Context and Persistent State Management: The platform maintains a layered storage architecture that optimizes both operational responsiveness and data durability through a unified infrastructure. Ephemeral operational context—including active reasoning chains, intermediate analytical results, and cross-agent communication artifacts—is maintained within session-scoped storage layers that provide immediate availability for cooperative agent processing while ensuring isolation between independent workflows. The session management infrastructure employs connection pooling and batch retrieval strategies to optimize throughput, enabling efficient context access across concurrent interactions without synchronous bottlenecks. The system implements hierarchical caching mechanisms that balance memory efficiency with access latency through in-memory memoization of agent instances, session metadata, and frequently accessed reference data, allowing the platform to maintain high responsiveness without explicit cache management overhead. Clinical evidence, diagnostic outputs, and audit-relevant records are segregated into persistent storage tiers with full transaction guarantees, ensuring that transient reasoning artifacts do not compromise data integrity requirements while maintaining complete traceability of diagnostic conclusions to source evidence.

This multi-tier approach optimizes cost and performance: transient data benefits from rapid-access characteristics, while persistent records maintain the integrity and regulatory compliance necessary for clinical governance and comprehensive audit trail preservation.

Disaster Recovery (DR) and High Availability (HA)

Disaster recovery planning is governed by two critical metrics: the Recovery Time Objective (RTO)—the maximum acceptable downtime—and the Recovery Point Objective (RPO)—the maximum acceptable data loss. Establishing appropriate RTO/RPO targets is essential for clinical platforms, as these metrics communicate operational maturity and reliability to clinical partners.

The platform leverages foundational AWS and Supabase capabilities to support tiered disaster recovery strategies:

• Backup and Restore: The platform implements automated backup mechanisms through Amazon S3 and AWS Backup, providing durable, cost-effective long-term archival and recovery for non-critical systems. This approach ensures baseline data protection while maintaining operational efficiency.

• High-Availability Architecture: Fargate deployment across multiple Availability Zones provides inherent redundancy and automatic failover for the core inference services. MongoDB replica sets operating across AZs ensure database-level resilience, supporting rapid recovery from transient failures without manual intervention.

• Future Resilience Enhancement: AgenticPet's architecture is designed to support implementation of more aggressive DR strategies as operational requirements evolve. The platform's containerized, multi-AZ design naturally accommodates advanced patterns such as Pilot Light or Active-Active configurations, enabling progressively faster recovery times as the platform scales. When implemented, such strategies would enable Recovery Time Objectives in the range of minutes rather than hours, significantly reducing diagnostic service interruption during disaster scenarios.

The platform's commitment to systematic infrastructure monitoring via CloudWatch, combined with its modular deployment architecture, establishes the foundation for continuous validation and refinement of disaster recovery capabilities. As the platform matures through clinical partnerships and operational experience, disaster recovery testing and automated failover processes can be progressively enhanced to achieve enterprise-grade recovery targets appropriate for critical clinical deployment.