The client sought to improve operational efficiency across complex, multi-tenant firewall environments. Manual workflows and API-driven tasks required deep expertise and significant time investment, limiting agility and scalability.
- Reduce operational overhead associated with manual firewall configuration, diagnostics, and troubleshooting
- Enable natural language interaction for routine administrative tasks across distributed firewall environments
- Securely integrate with protected firewall APIs while maintaining enterprise-grade security and data isolation
- Improve time-to-resolution for operators managing multi-tenant deployments
- Support thousands of users and high volumes of LLM requests with predictable cost and performance
- Align the solution with an AWS-first architecture strategy and cloud-native scalability requirements
We designed and implemented a cloud-native, agentic AI architecture on AWS to enable natural language–driven firewall management. The solution combines foundation models, managed Retrieval-Augmented Generation (RAG), and secure API orchestration to streamline operational workflows.
- Deployed a cloud-native, agentic AI architecture on Amazon EKS to orchestrate intent interpretation and task execution
- Used Amazon Bedrock foundation models with tool-use (function calling) to translate natural language prompts into secure firewall API actions
- Implemented managed Retrieval-Augmented Generation (RAG) using Amazon Bedrock Knowledge Base and Titan Text Embeddings V2
- Leveraged AWS OpenSearch Serverless for scalable vector-based semantic retrieval
- Built event-driven knowledge ingestion pipelines using Amazon S3 and AWS Lambda
- Secured all Bedrock integration using AWS PrivateLink and VPC Endpoints to maintain private, internet-isolated access
- Optimized performance and cost using Amazon ElastiCache (Redis, Multi-AZ) and Amazon RDS for PostgreSQL
- Selected AWS-managed services over self-hosted alternatives to reduce operational complexity and improve reliability
By deploying an agentic AI architecture on AWS, the client transitioned from manual firewall workflows to natural language–driven task execution.
- Reduced time-to-task completion for supported diagnostics and administrative workflows
- Increased operator productivity by minimizing dependency on deep product expertise
- Streamlined multi-tenant firewall management through automated API orchestration
- Improved responsiveness and workflow consistency across distributed environments
- Established a scalable, AWS-native architecture capable of supporting thousands of users
- Reduced operational complexity by leveraging managed AWS services instead of self-hosted AI infrastructure
While financial ROI was not directly measured, operational efficiency improvements are expected to contribute to lower support overhead, improved resource utilization, and long-term cost optimization.
