In response to chronic instrument downtime and loss of tacit knowledge, Natera introduced SWIFT, an agentic AI system that automatically processes lab support tickets using Amazon Bedrock AgentCore. Practitioners should note the move from a manually‑driven, expertise‑centric workflow to a managed, model‑driven pipeline that delivers diagnostics in minutes rather than hours.
Architecture Overview
SWIFT is organized into four logical layers:
- Event layer: Jira tickets are captured by Amazon EventBridge and placed onto Amazon SQS queues.
- Agent layer: Two agents—
swarm-agentfor autonomous report generation andswarm-hivefor interactive troubleshooting—share a common skill library and run inside the AgentCore managed runtime. - Context layer: The agents query a knowledge base built from vendor manuals, historical tickets, and operational data stored in Amazon DynamoDB.
- Observability and delivery layer: Execution traces, logs, and metrics are emitted to AWS X‑Ray and CloudWatch for end‑to‑end visibility.
The design keeps data flow simple: a ticket triggers an event, the appropriate agent consumes the message, enriches it with contextual knowledge, and returns a structured diagnostic report to the engineer.
Implementation Highlights
Key implementation choices include:
- Using the fully managed AgentCore runtime to avoid custom container orchestration and to leverage built‑in IAM integration for authentication and authorization.
- Storing the skill library and reference documents in DynamoDB, which provides low‑latency key‑value access without additional provisioning.
- Routing tickets through EventBridge and SQS, which decouples the ingestion point from the processing agents and supports scaling to the observed 65 tickets per day.
- Enabling model swapping within AgentCore without code changes, allowing the team to experiment with different foundation models as needed.
Performance metrics reported by the team show a 130× reduction in first‑response time and 96.2% automated coverage of incoming tickets, turning a 17.4‑hour average response into a matter of minutes.
Operational and Security Implications
From an operations perspective, the managed runtime eliminates the need for a custom scaling layer, reducing operational overhead and simplifying incident response. Observability is baked in via X‑Ray, giving engineers traceability across the event‑agent‑context chain.
Security is addressed through several built‑in controls:
- AgentCore enforces encryption at rest and in transit for all data handled by the agents.
- IAM integration provides role‑based access to the underlying AWS resources, removing the need for separate credential management.
- The team adopted a keyless deployment model, which aligns with clinical‑grade requirements by avoiding static secret distribution.
These controls mean that the platform inherits AWS‑level security guarantees without additional engineering effort.
Related CloudNinjas coverage: AWS.
What This Means For Practitioners
Adopting a managed agentic AI service like AgentCore can dramatically accelerate response times for high‑volume, knowledge‑intensive workflows while offloading infrastructure and security responsibilities to the provider. Teams should evaluate whether their ticketing or incident streams can be expressed as events, assess the feasibility of building a skill library from existing documentation, and verify that the built‑in observability meets their compliance and debugging needs. If those conditions align, the pattern demonstrated by Natera offers a concrete path to replace fragile, expertise‑centric processes with a scalable, auditable AI‑driven alternative.
