What challenges did Works Human Intelligence face before AI implementation?

Automate routine HR system operations after employee relocations and other updates. Move from a LangGraph proof of concept to an integrated multi-agent architecture. Reduce agent execution cost and operational monitoring overhead.

What was the impact of the AI solution?

The Browser Operation Agent achieved an 88% reduction in browser operation tokens. Cost per process was reduced by up to 97%. Switching from self-hosted Langfuse to AgentCore Observability reduced operational monitoring burden. The architecture change enables future expansion of sub-agents and more complex browser workflows.

Source

Works Human Intelligence builds Bedrock AgentCore commuting allowance and browser operation agents for HR system automation

Use case typeAI agentsUpdated May 27, 2026

Works Human Intelligence (WHI) develops and supports the integrated HR system COMPANY for major Japanese corporations and public interest corporations. The company built two AI agents with Amazon Bedrock AgentCore to automate routine HR operations, including commuting allowance approvals and browser-based HR system tasks.

Organization: Works Human Intelligence
Industry: Healthcare
Location: Japan
Published: May 2026

Reported outcomes

−97%

cost per processCost savings

−88%browser operation tokens

Strategic outcomes

Cost efficiencyReduced monitoring burdenScale & capacityEnabled future sub-agent expansionOther strategic outcomeAutomated HR approval and browser tasks

Catalog median for cost savings deployments: −44.5% across 344 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 3 of 3

1AI agents
2Workflow automation
3Agent orchestration

Automate routine HR system operations after employee relocations and other updates.
Move from a LangGraph proof of concept to an integrated multi-agent architecture.
Reduce agent execution cost and operational monitoring overhead.

Built a Commuting Allowance Agent that automates approval workflows and launches sub-agents on separate AgentCore Runtime instances.
Built a Browser Operation Agent that uses Strands Agents, a knowledge base of operation templates, and browser tools to generate operation manuals, execute HR system changes, and collect evidence.
Placed AgentCore Runtime inside a VPC with fixed IP access through a NAT gateway, and used Amazon Cognito and AWS Fargate for authentication and authorization.
Used Amazon Bedrock prompt caching, prompt and behavior optimization, model changes, Amazon S3 for short-term information, and AgentCore Observability for monitoring.

Technologies

Amazon Bedrock AgentCore AWS Fargate Amazon Cognito Amazon S3 Amazon Bedrock Prompt Caching AgentCore Observability Strands Agents SDK LangGraph Playwright MCP Amazon ECS

The Browser Operation Agent achieved an 88% reduction in browser operation tokens.
Cost per process was reduced by up to 97%.
Switching from self-hosted Langfuse to AgentCore Observability reduced operational monitoring burden.
The architecture change enables future expansion of sub-agents and more complex browser workflows.

Architecture

Two AI agents were built on Amazon Bedrock AgentCore. The Commuting Allowance Agent runs sub-agents on separate AgentCore Runtime instances with Amazon Cognito and AWS Fargate for authentication and authorization. The Browser Operation Agent uses Strands Agents, a knowledge base, Amazon S3, and fixed-IP VPC access via a NAT gateway. The workflow also uses Amazon Bedrock prompt caching and AgentCore Observability.

Implementation partners1

AWS Generative AI Innovation Center

Sources & evidence1

Groundedness: 4/5Type: Blog PostPublished: May 27, 2026Publisher: AWSEvidence: VendorConfidence: Medium

Primary source

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Customer evidence