Duolingo builds an agentic feature flag remover using Codex CLI and Temporal
Duolingo engineers needed agentic tooling to automate simple but repetitive tasks like feature flag removal, and lacked a clean, reliable pattern for building agents that operate directly on their codebase.
Two parallel implementations — one using langchain with an internal toolset called Baymax, and one using fast-agent with the Github MCP — were in development for 1–2 weeks before being abandoned when Codex CLI was released and worked immediately with a single prompt.
Development was described as quick, easy, and immediately successful — a prototype was running in approximately one day and a productionized version was ready within one week.
Frequently asked questions
What did this team achieve with this AI workflow?
Development was described as quick, easy, and immediately successful — a prototype was running in approximately one day and a productionized version was ready within one week.
What tools did this team use?
Temporal, Codex CLI, ECS, langchain, fast-agent, Baymax, Github MCP, Github.
What results were reported?
Prototype build time: ~1 day; Productionized version build time: ~1 week; Prior approach development time before abandonment: 1-2 weeks (source-reported, not independently verified).
What failed first in this deployment?
Two parallel implementations — one using langchain with an internal toolset called Baymax, and one using fast-agent with the Github MCP — were in development for 1–2 weeks before being abandoned when Codex CLI was rel…
How is this back office ops AI workflow structured?
User initiates via self-service UI → Fetch GitHub account name → Codex CLI performs flag removal → PR created and assigned.