Grab architecture: how Grab scaled (2 case studies)
We track 2 public engineering write-ups by Grab, published in 2026. They cover agent framework, crash triage, feature flags and LLM gateway. Stack mentioned: Android, BigQuery, Firebase Crashlytics, Go and GrabGPT Gateway. 2 of the 2 state concrete scale numbers, for example "millions of lines of Android code". Each card below links to our short summary and the original post.
App stability at scale with a layered defense
With many engineers and AI-assisted code landing in a weekly release of a superapp that has millions of lines of Android code, Grab stops crashes in layers. Lint rules (including custom ones written after post-mortems) and tests block merges, a mandatory one-week Grab Early Access build catches crashes before production, and a staged rollout over a week is automatically paused when the new version's crash rate looks abnormal. In production, on-call engineers first flip feature flags, then try server-side fixes, then hotfix, helped by a flag-rollout history dashboard and Crashlytics data exported to BigQuery.
Agent platform (Part 1): How we help Grab build and run AI agents at scale
Grab's internal AI agent work started as a Tech Infra support bot and grew into a company-wide platform: more than 500 services run on its agent framework (LLM-Kit), over 50 Model Context Protocol servers are registered on its remote MCP framework, and a single LLM gateway fronts every model call across the company, handling billions of tokens each month for production agents used daily by millions of merchants, drivers, and consumers. Part 1 covers how the original Go bot's reasoning plane and tool plane became reusable framework building blocks, and how problems that appeared around 500 agents—shared model control, safe tool reuse across teams, and evals after prompt changes—pushed the team toward the GrabGPT Gateway, remote MCP, and an evals platform covered in later parts.