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Creating an AI simulation assistant using agentic workflows on Amazon Web Services

Creating an AI simulation assistant using agentic workflows on Amazon Web Services



Simulations are vital tools for predicting outcomes, assessing risks, and making informed decisions across industries like manufacturing and healthcare. However, the process of running and analyzing simulations can be time-consuming and bottlenecked by the availability of specialized experts. To address these challenges, a generative AI-based “Simulation Assistant” demo application was developed using LangChain Agents and Anthropic’s Claude V3 large language model on Amazon Bedrock.

This Simulation Assistant aims to democratize simulation-driven problem-solving by enabling various personnel, beyond specialized teams, to launch and analyze simulations with the guidance of experts. By allowing a wider user base to run routine simulations, experts can focus on high-value tasks and streamline workflows, leading to increased efficiency and productivity.

The architectural diagram for the Simulation Assistant application features a containerized Streamlit web app deployed on a load-balanced AWS Fargate service. This setup allows for scalable and serverless simulation workflows with a chatbot-style interface, enabling users to interact with simulations through natural language prompts. The applications leverage AWS services like Lambda, Batch, DynamoDB, EventBridge, and Kendra to process queries, run simulations, store results, and trigger notifications.

The application implements the concept of “agentic behavior” of large language models (LLMs) through the use of “tools” that augment the capabilities of LLMs. These tools help LLMs perform tasks beyond direct generation using transformer networks, such as running simulations, querying databases, or triggering computations. The Simulation Assistant showcases how LLMs can break down complex tasks, engage with tools, and execute simulations in response to user queries.

Moving forward, the demo aims to integrate existing simulation codebases as tools, ensure simulation reproducibility and traceability, and establish guardrails for secure and responsible use of the framework. By leveraging generative AI and agentic LLM behavior, organizations can revolutionize their simulation workflows, drive better decision-making, and pave the way for new paradigms in human-machine collaboration.

For organizations interested in implementing these techniques in their workflows, reaching out to an AWS account team or emailing ask-hpc@amazon.com can provide guidance and support. Ultimately, solutions like the Simulation Assistant offer a glimpse into a future where simulations are more accessible, interactive, and empowering for organizations across diverse industries.

Article Source
https://aws.amazon.com/blogs/hpc/building-an-ai-simulation-assistant-with-agentic-workflows/

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