AWS shows synthetic monitoring with Nova Act and AgentCore
AWS published a tutorial on building synthetic monitoring using Amazon Nova Act and Amazon Bedrock AgentCore. The approach replaces traditional selector-based browser scripts with natural-language actions driven by Nova Act's multimodal model, which processes UI screenshots instead of DOM locators. AWS says early enterprise customers have seen over 90% accuracy on browser workflows with Nova Act.
Key points
- Nova Act uses vision-language model on screenshots, not DOM selectors, for resilience
- Architecture combines EventBridge Scheduler, AgentCore Runtime, Browser tool, and SNS
- Sample ecommerce journey runs in 2-4 minutes; ~8,640 runs per month at 5-minute cadence
The architecture uses EventBridge Scheduler to trigger runs every 5 minutes to hourly, AgentCore Runtime for serverless execution with session isolation, the AgentCore Browser tool for remote browser sessions, and SNS for failure alerts. A sample repository provides a complete implementation for a six-step ecommerce journey that typically completes in 2 to 4 minutes. AWS includes a deploy.py script and a CDK stack for production deployment with dead-letter queues and CloudWatch alarms.
Cost estimates for the sample six-step journey running every 5 minutes (about 8,640 invocations per month) cover AgentCore Runtime invocations, Browser tool sessions, Nova Act inference, EventBridge Scheduler calls, and SNS notifications. AWS recommends focusing on 3-5 critical workflows to avoid alert fatigue and using ephemeral browser sessions for test independence.
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Implementing synthetic monitoring using Amazon Nova Act
AWS Machine Learning Blog · 28 September 2026
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This text was published by AWS Machine Learning Blog and written by Sarath Krishnan. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗
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