# Agent platform (Part 1): How we help Grab build and run AI agents at scale

[Grab](https://yomu.fyi/company/grab) · Kendrick Tan · Jul 24, 2026

## Summary

Grab scaled its internal AI tooling by transforming the infrastructure behind a single technical support bot into a standardized agent framework named LLM-Kit. The original Go-based bot revealed critical bottlenecks, including the lack of automated evaluation, painful provider migrations, fragmented observability, and excessive production scaffolding overhead. To address these frictions, engineers developed a FastAPI template pre-wired with LangGraph ReAct agents, remote Model Context Protocol servers, and built-in ROUGE, BLEU, and LLM-as-judge evaluation suites. The framework centralizes model access through an OpenAI-compatible GrabGPT Gateway and unifies observability across services, outbound calls, and agent steps using OpenTelemetry. This extracted scaffolding reduced day-one production setup time from two weeks to roughly an hour while supporting hundreds of production agents.

## Takeaways

- Early agent deployments stalled because production overhead—such as OpenID Connect authentication, Vault secret management, and vector database integration—took weeks, compared to an afternoon for reasoning loop logic.
- LLM-Kit scaffolds services with pre-configured LangGraph StateGraph agents featuring a 30-second per-step timeout and day-one evaluation endpoints supporting ROUGE, BLEU, and LLM-as-judge metrics.
- Routing model requests through the OpenAI-compatible GrabGPT Gateway enables centralized provider switching, fallback routing, and cost attribution without requiring application code changes.

**Tags:** [Architecture](https://yomu.fyi/topic/architecture), [Developer Experience](https://yomu.fyi/topic/developer-experience), [LLMs](https://yomu.fyi/topic/llm), [Observability](https://yomu.fyi/topic/observability), [Python](https://yomu.fyi/topic/python)

[Read original post](https://engineering.grab.com/how-grab-builds-and-runs-ai-agents-at-scale)