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description: Grab's engineering team shares how they evolved a single internal support bot into a company-wide AI agent platform called LLM-Kit, now powering over 500...
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# Agent platform (Part 1): How we help Grab build and run AI agents at scale

**[Grab Tech Blog](https://daily.dev/sources/grab)** · 13 min read · 0 upvotes · 0 comments

## Summary

Grab's engineering team shares how they evolved a single internal support bot into a company-wide AI agent platform called LLM-Kit, now powering over 500 services. The post details the specific pain points encountered — lack of evaluations, slow model switching, fragmented observability, and weeks of boilerplate setup — and how each became a core framework primitive. LLM-Kit provides a scaffolded FastAPI+LangGraph template with pre-wired OIDC auth, Vault-based secrets, OpenTelemetry tracing, MCP server integration, gRPC service connectivity, and built-in eval endpoints (ROUGE, BLEU, LLM-as-judge). What previously took two weeks of production wiring now takes about an hour. The post is Part 1 of a series; Part 2 will cover the GrabGPT Gateway, remote MCP framework, and evals platform.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://engineering.grab.com/how-grab-builds-and-runs-ai-agents-at-scale>

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---

Tags: [#python](https://daily.dev/tags/python), [#ai-agents](https://daily.dev/tags/ai-agents), [#mcp](https://daily.dev/tags/mcp), [#langgraph](https://daily.dev/tags/langgraph), [#llm-observability](https://daily.dev/tags/llm-observability)

[View this post on daily.dev](https://daily.dev/posts/agent-platform-part-1-how-we-help-grab-build-and-run-ai-agents-at-scale-bdejj0ndh)

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