Extract, analyze, and migrate Kubernetes configurations from other cloud providers to Azure Kubernetes Service (AKS) using intelligent multi-agent orchestration powered by Azure OpenAI GPT-5.1, Microsoft Agent Framework, and MCP (Model Context Protocol) servers. This solution provides automated platform detection, multi-dimensional analysis, and expert-guided configuration transformation with comprehensive migration reporting.
Transform your Kubernetes workloads with confidence through AI-driven analysis covering security, networking, storage, and Azure Well-Architected Framework principles. Expert agents collaborate to ensure your migrated configurations are production-ready and optimized for Azure.
SOLUTION OVERVIEW | QUICK DEPLOY | MIGRATION SCENARIO | TECHNICAL DOCUMENTATION
This accelerator provides a complete enterprise migration platform leveraging Azure OpenAI GPT-5.1, Microsoft Agent Framework workflows, Azure Container Apps, Azure Blob Storage, Azure Storage Queue, and MCP (Model Context Protocol) servers. The solution consists of a React-based web application for file upload and validation, coupled with an intelligent multi-agent processing engine that analyzes and transforms Kubernetes configurations through event-driven batch processing pipelines.
The architecture follows enterprise-grade batch processing patterns with loosely coupled components, enabling organizations to migrate from managed cloud, enterprise distribution, and self-managed/on-prem Kubernetes platforms (for example: EKS, GKE/Anthos, OpenShift, Rancher/RKE/K3s, Tanzu, and upstream/on-prem Kubernetes) to Azure Kubernetes Service at scale while maintaining comprehensive audit trails and expert-level analysis quality.
The solution uses a sophisticated multi-agent orchestration pattern where specialized expert agents collaborate to:
- Platform Detection: Automatically identify source platform (other cloud providers platform)
- Multi-Dimensional Analysis: Technical Architect, Azure Expert, cloud platform Experts, and QA Engineer agents analyze configurations
- Design Transformation: Apply Azure Well-Architected Framework principles
- YAML Conversion: Expert YAML transformation with security, networking, and storage optimizations
- Documentation Generation: Comprehensive migration reports with expert insights
This solution enables enterprise-grade Kubernetes migration with the following capabilities:
-
Enterprise Batch Processing Architecture
Complete solution including React web application for file upload, validation, and queue management coupled with intelligent multi-agent processing engine -
Intelligent Platform Detection
Automatically identifies source Kubernetes platform/distribution (for example: EKS, GKE/Anthos, OpenShift, Rancher/RKE/K3s, Tanzu, and self-managed/on-prem Kubernetes) through configuration analysis and applies platform-aware migration strategies -
Multi-Agent Expert Orchestration
Specialized agents (Chief Architect, AKS Expert, platform experts, QA Engineer, Technical Writer, YAML Expert) collaborate using Microsoft Agent Framework group chat orchestration -
Workflow Engine
Each migration step (analysis, design, conversion, documentation) is executed as a step-based Agent Framework workflow with explicit executor chaining -
MCP Server Tool Integration
Agents access intelligent tools through Model Context Protocol servers for file operations, knowledge search, and specialized functions without direct model training -
Azure Well-Architected Framework Compliance
Configurations transformed according to Azure security, reliability, performance, cost optimization, and operational excellence principles -
Comprehensive Migration Reporting
Detailed reports capturing expert analysis, transformation decisions, and recommendations from multiple specialized agents
Orchestration flow: 4 workflow executors coordinate group chats and tools.
flowchart LR
%% Top-level orchestration + telemetry
TELEM["Agent & Process Status<br/>(telemetry)"]
COSMOS[("Cosmos DB<br/>telemetry/state")]
PROC["Process Orchestration<br/>Agent Framework WorkflowBuilder"]
TELEM --> COSMOS
PROC --- TELEM
%% Step lanes
subgraph S1["Step 1: Analysis"]
direction TB
S1EXEC["Analysis Executor"] --> S1ORCH["Analysis Chat Orchestrator<br/>(GroupChatOrchestrator)"] --> S1AGENTS["Agents:<br/>Chief Architect<br/>AKS Expert<br/>Platform experts (EKS/GKE/OpenShift/Rancher/Tanzu/OnPremK8s)"]
end
subgraph S2["Step 2: Design"]
direction TB
S2EXEC["Design Executor"] --> S2ORCH["Design Chat Orchestrator<br/>(GroupChatOrchestrator)"] --> S2AGENTS["Agents:<br/>Chief Architect<br/>AKS Expert<br/>Platform experts (EKS/GKE/OpenShift/Rancher/Tanzu/OnPremK8s)"]
end
subgraph S3["Step 3: YAML Conversion"]
direction TB
S3EXEC["Convert Executor"] --> S3ORCH["YAML Chat Orchestrator<br/>(GroupChatOrchestrator)"] --> S3AGENTS["Agents:<br/>YAML Expert<br/>Azure Architect<br/>AKS Expert<br/>QA Engineer<br/>Chief Architect"]
end
subgraph S4["Step 4: Documentation"]
direction TB
S4EXEC["Documentation Executor"] --> S4ORCH["Documentation Chat Orchestrator<br/>(GroupChatOrchestrator)"] --> S4AGENTS["Agents:<br/>Technical Writer<br/>Azure Architect<br/>AKS Expert<br/>Chief Architect<br/>Platform experts (EKS/GKE/OpenShift/Rancher/Tanzu/OnPremK8s)"]
end
PROC --> S1
S1 -->|Analysis Result| S2
S2 -->|Design Result| S3
S3 -->|YAML Converting Result| S4
If you want to get know more detail about Agentic Architecture, please take a look at this document: Agentic Architecture
Advanced AI Orchestration Patterns:
- Model: Azure OpenAI GPT-5.1 for advanced reasoning and analysis capabilities
- Framework: Microsoft Agent Framework for multi-agent orchestration and workflow execution
- Workflow Management: Agent Framework
WorkflowBuilderwith step executors (analysis → design → yaml → documentation) - Tool Access: MCP (Model Context Protocol) servers enabling intelligent tool selection and usage
MCP Server Integration:
- Internal MCP Servers (FastMCP): Blob I/O, DateTime, Mermaid validation, YAML inventory
- External MCP Servers: Microsoft Learn Docs (HTTP) and Fetch (
uvx mcp-server-fetch) - Intelligent Tool Selection: Agents choose tools based on context
If you'd like to customize the solution accelerator, here are some common areas to start:
Modifying Migration Analysis Prompts
Multi-Agent Orchestration Approach
The Container Migration Solution Accelerator supports development and deployment across multiple platforms and environments.
For Azure Deployment: Click here to launch the deployment guide
For Local Development:
- Local Development Setup Guide - Comprehensive setup instructions for Windows, Linux, and macOS
- Includes native Windows setup, WSL2 configuration, and cross-platform development tools
flowchart TB
%% --- Themed deployment view (v2) ---
%% Palette intentionally reuses colors already present in repo diagrams.
subgraph CLIENT["Client"]
direction TB
U["User / Browser"]
end
subgraph RUNTIME["Runtime"]
direction TB
subgraph APPS["Azure Container Apps"]
direction TB
FE["Frontend"]
API["Backend API"]
PROC["Processor (Queue Worker)"]
end
subgraph DATA["Data & Configuration"]
direction TB
Q[("Azure Storage Queue")]
BLOB[("Azure Blob Storage")]
COSMOS[("Azure Cosmos DB")]
APP_CFG[("Azure App Configuration")]
end
end
subgraph AI["AI"]
direction TB
AOAI[("Azure OpenAI<br/>Azure AI Foundry<br/>GPT-5.1")]
end
subgraph BUILD["Images & Identity"]
direction TB
ACR[("Azure Container Registry")]
ID["User Assigned Managed Identity"]
end
%% User flow
U -->|HTTPS request| FE
FE -->|HTTPS request| API
%% Job intake + processing
API -->|Enqueue job| Q
Q -->|Dequeue job| PROC
%% Artifacts + state
PROC -->|Write artifacts| BLOB
PROC -->|Write state| COSMOS
%% Configuration
API -->|Read config| APP_CFG
PROC -->|Read config| APP_CFG
%% LLM usage
PROC -->|LLM call| AOAI
%% Image pulls
ACR -->|Pull image| FE
ACR -->|Pull image| API
ACR -->|Pull image| PROC
%% Identity usage
ID -.-> FE
ID -.-> API
ID -.-> PROC
%% Theme styling (monotone + subtle accents)
classDef core fill:#ffffff,stroke:#111827,color:#111827,stroke-width:1px;
classDef store fill:#e0f2fe,stroke:#0284c7,color:#111827,stroke-width:1px;
classDef frame fill:#f8fafc,stroke:#94a3b8,color:#111827,stroke-width:1px;
class U,FE,API,PROC core;
class Q,BLOB,COSMOS,APP_CFG,AOAI,ACR store;
class ID core;
style CLIENT fill:#f8fafc,stroke:#94a3b8,color:#111827;
style RUNTIME fill:#f8fafc,stroke:#94a3b8,color:#111827;
style APPS fill:#ffffff,stroke:#94a3b8,color:#111827;
style DATA fill:#ffffff,stroke:#94a3b8,color:#111827;
style AI fill:#e0f2fe,stroke:#0284c7,color:#111827;
style BUILD fill:#e0f2fe,stroke:#0284c7,color:#111827;
⚠️ Important: Check Azure OpenAI GPT-5.1 Availability Model availability and quotas vary by region and subscription. Check the Azure OpenAI models catalog before deploying: https://learn.microsoft.com/azure/ai-services/openai/concepts/models
To deploy this solution accelerator, ensure you have access to an Azure subscription with the necessary permissions to create resource groups, resources, app registrations, and assign roles at the resource group level. This should include Contributor role at the subscription level and Role Based Access Control role on the subscription and/or resource group level.
Required Azure Services:
| Service | Description | Pricing |
|---|---|---|
| Azure OpenAI Service | Provides REST API access to the GPT-5.1 model for advanced reasoning and analysis | Pricing |
| Azure Container Apps | Runs containerized migration processor without managing infrastructure | Pricing |
| Azure Blob Storage | Stores source configurations, processed files, and migration reports | Pricing |
| Azure App Configuration | Manages application settings and agent configurations | Pricing |
| Azure Queue Storage | Handles event-driven processing and agent communication | Pricing |
Pricing varies per region and usage, so it isn't possible to predict exact costs for your usage. The majority of the Azure resources used in this infrastructure are on usage-based pricing tiers.
Use the Azure pricing calculator to calculate the cost of this solution in your subscription.
Model Access Requirements:
- Availability varies: GPT-5.1 availability may vary by region and subscription.
- Registration requirements: Registration is required for access to
gpt-5.1,gpt-5.1-codex, andgpt-5.1-codex-max. Request access here: https://aka.ms/oai/gpt5access. For region availability details, see Model summary table and region availability. - Quota management: Ensure sufficient quota for batch processing.
A DevOps engineer at a multi-platform enterprise manages Kubernetes workloads across managed cloud and self-managed/on-prem clusters (for example: GKE, EKS, and on-prem Kubernetes) and needs to migrate critical applications to Azure Kubernetes Service (AKS) following company cloud consolidation strategy.
The engineer has dozens of complex Kubernetes configurations with intricate networking, storage, and security requirements. Manual migration analysis would take weeks and risk configuration errors that could impact production workloads.
Using the Migration Solution Accelerator, the complete processing flow works as follows:
- Upload via Web Application: Engineer uploads Kubernetes configuration files through the React-based Migration Web Application
- Automated File Inspection: Web application performs comprehensive validation:
- File format and content-type verification
- YAML syntax validation
- Platform consistency checks (prevents mixing incompatible source platforms in a single batch)
- Queue Generation: After successful inspection, the system generates processing jobs with unique identifiers and submits them to Azure Storage Queue
- Migration Processor Activation: The multi-agent migration processor (this solution) monitors the queue, picks up processing jobs, and begins intelligent analysis
⚠️ Important Note on Processing: Processing is queue-driven. Concurrency is configurable (default is a single worker), so uploads may run sequentially or in parallel depending on deployment settings.
Once the migration processor receives a queue message, expert AI agents automatically:
- Platform Detection: Identify the most likely source Kubernetes platform/distribution (e.g., EKS/GKE/OpenShift/Rancher/Tanzu/On-prem) based on analyzed content
- Multi-Expert Analysis: Technical Architect analyzes overall architecture, while the matching platform expert(s) identify migration challenges and Azure Expert applies Well-Architected Framework principles
- Quality Validation: QA Engineer validates transformation logic and identifies potential issues
- YAML Transformation: Expert YAML agent converts configurations with security, networking, and storage optimizations
- Comprehensive Documentation: Generate detailed migration reports capturing all expert insights and transformation decisions
The solution provides enterprise-grade capabilities:
- Batch Processing: Handle multiple migration projects simultaneously through queue-based processing
- API Integration: External systems can leverage the migration engine through programmatic queue submission
- Scalable Processing: Independent scaling of web interface and processing engine components
- Fault Tolerance: Queue-based architecture ensures reliable processing with retry capabilities
Thanks to this enterprise batch processing architecture and AI-powered multi-agent orchestration, migrations that previously took weeks are completed in hours with higher accuracy, comprehensive documentation, and full audit trails.
⚠️ Note: This solution uses Azure OpenAI GPT-5.1 for advanced reasoning. Model availability and access requirements vary by region and subscription. Sample configurations in this repository are for demonstration purposes.
This solution provides significant value through intelligent automation:
- Accelerated Migration Timeline: Reduce weeks of manual analysis to hours of automated processing
- Expert-Level Analysis: Multiple specialized AI agents provide comprehensive platform expertise
- Risk Reduction: Automated validation and Azure best practices reduce migration risks
- Comprehensive Documentation: Detailed reports capture all transformation decisions
- Cost Optimization: Azure Well-Architected Framework principles optimize cloud spend
- Scalable Process: Handle multiple migration projects with consistent quality
This solution implements advanced multi-agent patterns using Microsoft Agent Framework group chat orchestration:
Expert Agent Specializations:
- Technical Architect: Overall architecture analysis and design decisions
- Azure Expert: Azure-specific optimizations and Well-Architected Framework compliance
- GKE Expert: Google Kubernetes Engine (GKE/Anthos) specific knowledge and migration patterns
- EKS Expert: Amazon Elastic Kubernetes Service expertise and AWS-to-Azure translations
- OpenShift Expert: Red Hat OpenShift specific patterns and transformations
- Rancher Expert: Rancher/RKE/RKE2/K3s patterns and migration considerations
- Tanzu Expert: VMware Tanzu/TKG patterns and migration considerations
- OnPremK8s Expert: Upstream/self-managed/on-prem Kubernetes patterns and common on-prem dependencies
- QA Engineer: Validation, testing strategies, and quality assurance
- YAML Expert: Configuration transformation and syntax optimization
Workflow Integration: Each migration step is implemented as an Agent Framework workflow with explicit executor chaining:
flowchart TB
%% Migration flow (step-oriented)
subgraph WF[" "]
direction TB
subgraph ROW[" "]
direction LR
subgraph A["Analysis Process"]
direction TB
A1["• Platform Detection<br/>• Technical Architecture Review<br/>• Source Configuration Analysis<br/>• Migration Complexity Assessment"]
A2["Agents:<br/>• Chief Architect<br/>• AKS Expert<br/>• Platform Expert(s)"]
A1 --> A2
end
subgraph D["Design Process"]
direction TB
D1["• Azure Well-Architected Framework<br/>• Target Architecture Design<br/>• Service Mapping Strategy<br/>• Security & Compliance Review"]
D2["Agents:<br/>• Chief Architect<br/>• AKS Expert<br/>• Platform Expert(s)"]
D1 --> D2
end
subgraph C["Conversion Process"]
direction TB
C1["• YAML Transformation<br/>• Azure Service Configuration<br/>• Resource Optimization<br/>• Validation & Testing"]
C2["Agents:<br/>• Chief Architect<br/>• Azure Expert<br/>• AKS Expert<br/>• YAML Expert<br/>• QA Engineer"]
C1 --> C2
end
subgraph DOC["Documentation Process"]
direction TB
DOC1["• Migration Report Generation<br/>• Expert Recommendations<br/>• Implementation Guide<br/>• Post-migration Checklist"]
DOC2["Agents:<br/>• Technical Writer<br/>• All Experts"]
DOC1 --> DOC2
end
A -->|Architecture Insights| D
D -->|Design Specifications| C
C -->|Converted Configurations| DOC
end
end
MCP Server Integration: Agents access tools through Model Context Protocol servers for intelligent capability extension:
- Blob Operations MCP: File reading/writing and artifact management (Azure Blob Storage)
- Microsoft Learn Docs MCP (HTTP): Documentation search/retrieval and best-practices lookup
- DateTime MCP: Timestamp generation and time-based operations
- Fetch MCP: URL fetching for validation (for example: verifying references)
- Mermaid Validation MCP: Validate Mermaid diagrams generated during design/docs steps
- YAML Inventory MCP: Enumerate converted YAML objects for runbooks and reports
Check out related Microsoft solution accelerators:
| Solution | Description |
|---|---|
| Content Processing Solution Accelerator | Process and extract data from unstructured documents using AI |
| Document Knowledge Mining | Extract insights from documents with AI-powered search |
Have questions, find a bug, or want to request a feature? Submit a new issue on this repo and we'll connect.
Please refer to Transparency FAQ for responsible AI transparency details of this solution accelerator.
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To opt out, perform the following steps:
- Set the environment variable
AZURE_DEV_COLLECT_TELEMETRYtonoin your environment prior to deploying. - Set the
enableTelemetryparameter value inmain.parameters.jsontofalseprior to deploying.
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