Amazon Web Services (AWS)
We used AWS to host the application and connect it with managed AI services. This allowed us to build the platform without having to set up and maintain our own infrastructure for running an AI model.
AI Workflow Platform on Amazon Bedrock
Omax Tech developed a web-based AI platform using Amazon Bedrock, Claude Opus 4.4, and Strands Agents, enabling users to interact with AI through a customized dashboard
Project Year
2026
Industry
Software/IT
AI-powered conversational interface
Managed AI integration with Amazon Bedrock
Flexible foundation for future AI workflows
Overview
This project involved building a web-based AI platform where users can interact with AI through a simple, customized dashboard. The solution uses Amazon Bedrock, Anthropic's Claude Opus 4.4 model, and the Strands Agents SDK to process user prompts and generate AI-powered responses.
The goal was to make AI accessible through an easy-to-use interface that could fit into the client's business workflows. By using AWS managed services, we were able to build the application without the need to host or manage our own AI model infrastructure.
The platform provides a solid foundation for adding more AI features and connecting intelligent capabilities with existing business applications as the client's needs grow.
Client Challenge
Many businesses are looking for practical ways to use AI in their daily work, whether it's finding information faster, analyzing data, improving productivity, or reducing repetitive tasks.
Public AI tools can be useful, but businesses often need something more tailored to their needs, with a custom interface and the flexibility to connect AI with their existing applications and workflows.
To meet these needs, we developed a dedicated AI dashboard that allows users to interact with AI through a web-based application.
The main goals of the project were to:
Build a simple, user-friendly interface for AI conversations.
Connect the application to an advanced AI model using AWS services.
Use an agent-based approach to manage AI interactions.
Make it easier to add business-specific tools and workflows in the future.
Give the client flexibility to manage the application's design, backend, and integrations.
Solution Delivered
The solution is a web-based AI application that connects a custom dashboard to an AI agent powered by Amazon Bedrock and Claude Opus 4.4.
Users interact with the platform through a conversational interface. Their requests are processed by the application backend and passed to the configured Strands agent.
The agent uses Claude Opus 4.4 to interpret instructions and generate responses. Where tools are configured, the agent can also coordinate tool execution to support more complex tasks. The resulting response is returned to the dashboard, where it is presented to the user.
The architecture separates the user interface, application logic, agent orchestration, and foundation model access, creating a modular structure that can be developed and maintained independently.
Technology Stack
Amazon Bedrock
Amazon Bedrock connects the application to the selected AI model, Claude Opus 4.4. The backend uses AWS APIs to send user prompts to the model and receive its responses.
Anthropic Claude Opus 4.4
Claude Opus 4.4 powers the platform’s conversational AI, handling prompts to analyze information, summarize content, and generate text. Through Amazon Bedrock, the application can access the model without hosting or managing its infrastructure.
Strands Agents SDK
We used the Strands Agents SDK to build the agent layer of the application. It helps manage interactions between the AI model, its instructions, and any tools configured for the agent. This also provides a starting point for adding more advanced AI workflows as the application evolves.
Web Dashboard
The web dashboard lets users interact with AI, enter prompts, submit requests, and view responses through a dedicated interface. It gives the client more control over the user experience and room for future improvements.
High-Level Architecture
The application works through a straightforward flow, from the moment a user submits a prompt to the point where the AI response appears on the dashboard.
Request flow at a glance:
User submits a prompt: The user enters a question or instruction through the web dashboard.
Backend processes the request: The backend receives the prompt and performs the necessary validation and access checks.
Strands agent handles the request: The backend passes the request and relevant context to the Strands agent for processing.
AI model is called: The agent uses Amazon Bedrock to send the request to Claude Opus 4.4.
Tools are used when needed: If the agent has access to configured tools, it can use them to gather information or complete specific tasks.
AI generates a response: The model processes the request, and the agent prepares the result.
Response appears on the dashboard: The backend sends the response back to the web interface, where the user can view it.
This setup keeps the dashboard, backend, and AI integration as separate parts of the application. It makes the platform easier to maintain and allows new AI features to be added without needing to redesign the entire application.
Key Technical Considerations
Agent orchestration
The Strands Agents SDK provides a structured approach to implementing agent behavior, instructions, and tool interactions. This can support more advanced workflows as additional capabilities are integrated.
Model integration
Amazon Bedrock provides managed access to Claude Opus 4.4. The application can therefore focus on its business logic and user experience rather than managing the foundation model's serving infrastructure.
Application security
A production-ready implementation should enforce authentication and authorization, protect API credentials, validate incoming requests, and ensure that users can access only the data they are authorized to view.
Conversation management
Conversation history and contextual information must be handled according to the application's requirements. Appropriate state management helps preserve relevant context across interactions while maintaining separation between conversations.
Performance and cost management
Model inference introduces variable latency and usage-based costs. Monitoring response times, token consumption, errors, and request volumes can help the team evaluate performance and manage operational expenditure.
Conclusion
This project demonstrates how AWS services and modern AI tools can be used to build a customized AI platform for business use. By combining Amazon Bedrock, Claude Opus 4.4, and the Strands Agents SDK, we developed a web-based dashboard where users can submit prompts and receive AI-generated responses.
Using managed AWS services removes the need to host the AI model independently and gives the client a flexible foundation for future development. The platform can be extended with additional tools, business-specific workflows, and new AI capabilities as requirements evolve.
Overall, the solution provides a practical starting point for integrating AI into business applications while keeping the application and its underlying integrations under the client's control.
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