Hands-free first notice of loss: Using Strands Agents and Amazon Bedrock AgentCore Browser Tool for intelligent claims intake
Publish Date: 2026-06-09 12:43:00
Source Domain: aws.amazon.com
Turning multimodal first notice of loss (FNOL) evidence into tagged, decision-ready intake so adjusters start with context instead of raw artifacts.
Manual FNOL processing consumes significant expert time on repetitive tasks because unstructured, multimodal evidence must be interpreted through portals designed for human interaction. Photos captured in the field, walkaround videos, scanned documents, and dictated or recorded notes all enter the system at intake, where decisions directly influence claim cycle time, downstream accuracy, and customer experience.
Across insurance lines, this moment is deceptively complex. FNOL intake is often described as “just opening a claim,” but in practice, it’s where large volumes of unstructured data must be interpreted, validated, and correlated before any meaningful decisions can begin.
The challenge is significant: claims professionals spend excessive time on repetitive intake validation. Navigating portals, verifying evidence completeness, and interpreting artifacts before applying their expertise to higher-value decisions takes considerable time. Industry observations suggest that intake validation can consume a substantial share of an adjuster’s time during initial claim processing, with typical submissions requiring meaningful screen work before assessment can begin. During volume spikes from catastrophic events or seasonal surges, these delays compound, creating backlogs that slow claim resolution and impact customer experience.
In this post, we demonstrate how a hands-free FNOL intake system combines agents built with the Strands Agents SDK for domain reasoning with Amazon Bedrock AgentCore Browser Tool for live portal interaction. This approach preserves human expertise while removing repetitive screen work.
The solution combines two complementary capabilities:
Strands Agents is an open source SDK that takes a model-driven approach to building generative AI…
