At Natera, a leader in cell-free DNA (cfDNA) testing, keeping lab instruments up and running is critical, because equipment downtime disrupts sample processing. Natera’s lab engineering team, led by Ariel Jirau worked with AWS to build SWARM (Systematic Workflow Analytics and Resolutions Manager), an agentic AI system for lab operations.
This post walks through how the Natera team built an agentic AI system for lab operations using Amazon Bedrock AgentCore that captures and deploys institutional knowledge at scale. It describes the full architecture, from knowledge base construction to automated system deployment and evaluation, so you can apply the same patterns to your own operational workflows.
SWARM delivers about 130x faster first response time and 96.2 percent automated coverage across support tickets, giving engineers immediate access to institutional knowledge the moment a ticket arrives.
Natera’s lab operations rely on a wide range of precision instruments spanning multiple equipment families, each with its own failure modes, vendor documentation, and resolution patterns. The service engineering team responsible for maintaining this infrastructure is organized around deep individual expertise rather than standardized playbooks. When a senior technician who had spent years diagnosing a particular instrument family departed, the institutional knowledge they carried was effectively lost. No system existed to capture, organize, or deploy that expertise at scale.
4 hours, instrument downtime delayed lab samples and patient reporting. Hiring more engineers couldn’t keep pace. SWARM had to respond in minutes, deliver accurate diagnostics across a wide range of equipment types, and improve continuously through a structured evaluation loop.
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