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ai-agents

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Basic Information

This repository provides a tutorial, code samples and step-by-step instructions to build an AWS Solutions Architect generative AI agent using Amazon Bedrock. It is designed as a hands-on workshop showing how to combine multiple tools into a single agent that can answer architecture questions from a knowledge base, analyze and explain architecture diagrams using a multimodal foundation model, generate Infrastructure-as-Code (Terraform) for an MVP and estimate monthly costs. The materials cover prerequisites, how to prepare S3 data and create a Bedrock knowledge base, how to build and store embeddings/vector indexes, how to craft prompts for IaC generation, how to run Infracost in Docker to estimate expenses, and how to package the agent as a Docker image, push to ECR and deploy as a Lambda-backed Bedrock Agent with an OpenAPI schema.

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App URL
https://github.com/viktoriasemaan/ai-agents
Github Repository
https://github.com/viktoriasemaan/ai-agents/blob/main/README.md

App Details

Features
The repo is organized around three integrated tools and an agent orchestration layer. Tool 1 is a RAG Q&A chatbot that uploads AWS whitepapers and diagrams to S3, creates a Bedrock knowledge base, selects embedding models and a vector store and uses a foundation model to answer queries. Tool 2 uses Claude 3 multimodal capabilities to explain diagrams and generate Terraform code with a tailored prompt ending and stores results in S3. Tool 3 runs Infracost in a Docker image to produce monthly cost breakdowns and aggregates service line items. Integration guidance includes a Dockerfile (lambda base), ECR push, Lambda deployment, IAM permissions for Bedrock and S3, an OpenAPI schema with endpoints (/iac_gen, /iac_estimate_tool, /answer_query) and trace logging for agent actions.
Use Cases
This project helps solution architects and builders prototype an end-to-end generative agent for cloud architecture tasks. It automates retrieval of authoritative documentation via a knowledge base, leverages multimodal LLM vision to interpret diagrams, produces Terraform scaffolding for MVPs and generates cost estimates so teams can evaluate trade-offs before deployment. The agent orchestration and OpenAPI actions let Bedrock call Lambda tools programmatically, enabling reproducible workflows, S3-based artifact storage for traceability and developer-friendly tests in example scripts. The repo shortens iteration cycles for design, reduces manual IaC drafting and cost calculation, and provides a deployable pattern for experimenting with different foundation models and vector stores within an AWS environment.

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