Back office ops · Production

Garner Health accelerates complex SRE infrastructure development with Augment Code

The problem

Garner Health's SRE team faced complex, always-on ETL pipelines and rapid team growth, with ambitious infrastructure ideas—such as dynamic resource provisioning and state drift detection—too large to prototype in spare cycles, all under strict HIPAA compliance requirements.

First attempt

Garner Health evaluated GitHub Copilot, Cursor, and Windsurf before choosing Augment Code; Cursor was described as sluggish and none of the alternatives satisfied HIPAA compliance requirements.

Workflow diagram · grounded in source
1
Complex infrastructure requirement
trigger
“Ambitious infrastructure ideas (dynamic resource provisioning, state drift detection) that were too big to prototype in spare cycles.”
2
AI code generation in IDE
ai_action
“A weekend POC: Augment generated ≈ 10 K lines of production-ready Go for Forrest's dynamic-provisioning project—something he'd postponed for years.”
3
Org-level coding standards
validation
“With Augment guidelines and MCP integration, we can coordinate coding standards at an organizational level.”
4
Developer focuses on architecture
output
“Augment holds the whole system in context, so I can focus on architecture and outcomes”
Reported outcome

Augment Code generated approximately 10,000 lines of production-ready Go for a dynamic-provisioning project that had been postponed for years, and 60 developers onboarded in two days with positive feedback and zero IDE switches.

Reported metrics
lines of production-ready Go generated≈ 10 K lines
Developers onboarded60
Onboarding durationtwo days
Manual interventionsreduced the number of manual interventions
Show all 6 reported metrics
lines of production-ready Go generated≈ 10 K lines
developers onboarded60
onboarding durationtwo days
manual interventionsreduced the number of manual interventions
team capacity for innovationsignificantly enhancing the team's capacity for innovation
developer time on syntax vs architectureI don't have to deliberate on language semantics
Reported stack
Augment CodeMCPJetBrainsVS CodeVim
Source
https://www.augmentcode.com/customers/garner-health-augment-code
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Augment Code generated approximately 10,000 lines of production-ready Go for a dynamic-provisioning project that had been postponed for years, and 60 developers onboarded in two days with positive feedback and zero ID…

What tools did this team use?

Augment Code, MCP, JetBrains, VS Code, Vim.

What results were reported?

lines of production-ready Go generated: ≈ 10 K lines; Developers onboarded: 60; Onboarding duration: two days; Manual interventions: reduced the number of manual interventions (source-reported, not independently verified).

What failed first in this deployment?

Garner Health evaluated GitHub Copilot, Cursor, and Windsurf before choosing Augment Code; Cursor was described as sluggish and none of the alternatives satisfied HIPAA compliance requirements.

How is this back office ops AI workflow structured?

Complex infrastructure requirement → AI code generation in IDE → Org-level coding standards → Developer focuses on architecture.

Related back office ops cases