Multi Agent Custom Automation Engine Solution Accelerator

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Basic Information

This repository provides a solution accelerator for building a multi-agent automation engine that helps organizations automate complex, cross-departmental business processes. It delivers an opinionated, deployable reference architecture that coordinates multiple specialized AI agents to plan, execute, and validate tasks based on user input. The accelerator is built to run on Azure and integrates services such as Azure OpenAI Service, Azure Container Apps, Azure Cosmos DB, and Azure Container Registry. It includes deployment guidance, customization points for scenarios and agents, and supporting documentation to help teams adapt the pattern to their own processes. The repo is intended as a starting point for engineering teams and solution architects who want a reusable framework to orchestrate agent workflows rather than a single end-user application.

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App Details

Features
The solution implements an agentic orchestration architecture with specialized agents that collaborate to analyze, plan, execute, and validate work. It provides ready-made integration with Azure OpenAI Service for model access and with Azure Container Apps to host a web frontend. Persistent metadata and results storage is supported through Azure Cosmos DB and container images are managed via Azure Container Registry. The repo offers quick deploy scripts and a deployment guide, customization documentation for scenario and agent adaptation, security guidance using Key Vault and Managed Identity, and references to Semantic Kernel and Azure AI Foundry for extensibility. The accelerator emphasizes scalability, multi-agent validation, and reusable components to accelerate enterprise adoption of GenAI.
Use Cases
This accelerator reduces the effort required to design and deploy coordinated AI workflows by providing a tested architecture, automation, and documentation. It helps organizations improve process efficiency by automating task coordination, reduces human error through multi-agent validation, and optimizes resource use by freeing people to focus on specialized work. The pattern is industry-agnostic and intended to scale GenAI capabilities across use cases while controlling deployment and operational costs via Azure services. Teams benefit from built-in security practices such as Key Vault and Managed Identity, guidance on quota and cost management, and examples that simplify customization and faster rollout of automated business processes.

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