Natera deploys the SWARM agentic AI system for diagnosing laboratory equipment on the Amazon Bedrock AgentCore platform
Natera built, with the help of AWS, an agentic AI system called SWARM on the Amazon Bedrock AgentCore platform, which, according to the company, made first response to a support ticket 130x faster and automated 96.2% of tickets in its laboratory operations.
Natera, an operator of clinical laboratories focused on cfDNA testing, developed an agentic AI system called SWARM (Systematic Workflow Analytics and Resolutions Manager) in collaboration with AWS for diagnostics and automation of laboratory operations. The system was created in response to the problem that when senior technicians left, institutional knowledge about diagnosing specific types of equipment disappeared, and the company had no system to capture that knowledge or make it accessible to other engineers. According to the company, the service engineering group processed over 65 tickets a day across three Jira queues, with an average first-response time of 17.4 hours, and hiring additional engineers could not keep pace with demand.
SWARM is built on Amazon Bedrock AgentCore, AWS's fully managed service for running AI agents, which, according to the company, provided a ready-made runtime with identity management and the ability to change models without rewriting the agent's logic. The system has two interconnected modes sharing a common skills library: swarm-agent autonomously generates diagnostic reports even before an engineer opens a ticket, while swarm-hive lets engineers interact in real time, upload images, and browse alternative troubleshooting steps.
According to the company, the system achieves roughly 130x faster first response (from 17.4 hours down to minutes) and 96.2% automated coverage of support tickets. Deployment operates without long-lived access keys — authentication goes through OIDC and AWS STS with temporary credentials, operation is restricted to a private VPC network, and secrets are automatically rotated every 30 days. The system runs on HIPAA-eligible AWS services under a Business Associate Agreement, and according to the company, no identifiable patient data reaches either the vector index or the language model — tickets contain only metadata about equipment and workflows.
The source article further describes the document-processing architecture (embeddings via Amazon Titan Text Embeddings and Amazon Bedrock Knowledge Bases), but this section ends mid-description in the source and was not available in full. See the source article for details.
Why it matters
This case shows a concrete deployment of agentic AI in a regulated clinical environment, where emphasis is placed on security (no long-lived keys, HIPAA compliance, separation of patient data from the model) and on solving a specific operational problem — the loss of knowledge when key employees leave. For companies running similarly specialized service or support operations, it is relevant as a reference architecture for capturing institutional know-how in a knowledge base and making it accessible via an agent connected to a ticketing system.
Relevant practical impact
What this means
For a business
By deploying the SWARM system, Natera, according to its own figures, significantly shortened the time to first response on service tickets and automated most of their processing, thereby addressing the risk of losing institutional knowledge when key technicians leave and reducing the need to keep increasing the number of service engineers.
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