Haymaker Azure Workloads

Azure infrastructure workloads for the Agent Haymaker platform. This package provides goal-seeking agents that deploy, operate, and clean up Azure infrastructure scenarios – generating realistic telemetry for security and operations testing.

How It Works

Each workload scenario follows a three-phase lifecycle:

Deploy – Provision Azure resources (VMs, databases, networks, etc.) using Azure CLI commands
Operate – Run the infrastructure for a configurable duration, generating telemetry and monitoring data
Clean up – Remove all resources using tag-based tracking to ensure nothing is left behind

An optional LLM-enhanced agent mode adds adaptive error recovery, goal evaluation, and dynamic operations command generation.

Quick Start

# Install the workload via haymaker CLI
haymaker workload install https://github.com/rysweet/haymaker-azure-workloads

# Or install via pip
pip install haymaker-azure-workloads

# Deploy a scenario
haymaker deploy azure-infrastructure --config scenario=linux-vm-web-server

# With custom duration and region
haymaker deploy azure-infrastructure \
  --config scenario=linux-vm-web-server \
  --config duration_hours=4 \
  --config region=westus2

# Monitor a running deployment
haymaker status <deployment-id>
haymaker logs <deployment-id> --follow

# Clean up resources
haymaker cleanup <deployment-id>

Available Scenario Categories

Compute

linux-vm-web-server – Ubuntu VM with Nginx
windows-vm-iis – Windows Server with IIS
app-service-python – Python web app on App Service
azure-functions-http – HTTP-triggered Azure Functions
vm-scale-set – Virtual Machine Scale Set

Databases

mysql-wordpress – MySQL with WordPress
postgresql-django – PostgreSQL with Django app
cosmos-db-api – Cosmos DB with REST API

Networking

virtual-network – VNet with subnets and NSGs
load-balancer – Load Balancer with backend pool
application-gateway – Application Gateway with WAF

Security

key-vault-secrets – Key Vault secret management
managed-identity – Managed Identity with RBAC

AI/ML

cognitive-services – Cognitive Services APIs
azure-openai – Azure OpenAI deployment
ml-workspace – Machine Learning workspace

Documentation

LLM-Enhanced Agents – Adaptive error recovery, goal evaluation, and operations command generation
Scenarios – How scenarios work, three-phase execution, and creating custom scenarios
Configuration – All workload options, environment variables, and workload manifest format

Requirements

Azure subscription with Contributor role
Azure CLI installed and authenticated
Agent Haymaker platform installed

Development

git clone https://github.com/rysweet/haymaker-azure-workloads
cd haymaker-azure-workloads
pip install -e ".[dev]"
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