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

DevFeed: [Agent platform (Part 1): How we help Grab build and run AI agents at scale](<https://devfeed.tech/articles/agent-platform-part-1-how-we-help-grab-build-and-run-ai-agents-at-scale-1250.md>)

Original publisher: [Read original article](<https://engineering.grab.com/how-grab-builds-and-runs-ai-agents-at-scale>)

Author: Kendrick Tan

Published: 2026-07-24T00:00:10Z

Content type: article

Language: en

Sources: [Grab Tech](<https://devfeed.tech/sources/grab-tech.md>)

Topics: [AI Bots](<https://devfeed.tech/topics/ai-bots.md>), [AI Chat](<https://devfeed.tech/topics/ai-chat.md>), [MSP MCP](<https://devfeed.tech/topics/msp-mcp.md>)

Tags: [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [design](<https://devfeed.tech/tags/design.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [experiment](<https://devfeed.tech/tags/experiment.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [go](<https://devfeed.tech/tags/go.md>), [llm](<https://devfeed.tech/tags/llm.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [platform](<https://devfeed.tech/tags/platform.md>), [scale](<https://devfeed.tech/tags/scale.md>)

## AI overview

Grab describes the first part of its AI-agent platform story: a Tech Infra support bot that evolved from operational automation into a GPT-powered Level-0 support layer. The article outlines its reasoning and tool planes and explains how scaling challenges informed the LLM-Kit framework.

## Source excerpt

Part 1: From one support bot to a framework At Grab, AI agents have evolved from interesting team prototypes into production services used every day by millions of merchants, drivers, and consumers. Today, more than 500 services run on our internal agent framework, over 50 Model Context Protocol (MCP) servers are registered on our remote MCP framework, and a single Large Language Model (LLM) gateway fronts every model call across the company, handling billions of tokens each month. None of this was designed up front. It began as the plumbing behind one internal support bot, which then expanded because the same problems kept resurfacing for every team trying to ship an agent. This series tells the story of what the platform eventually became. This Part 1 of the blog focuses on the beginning: the architecture of our AI support bot, the specific pain points we hit while scaling and iterating on it, and how each of those failures became a core building block in the framework we now call LLM-Kit. The bot that started it Imagine you have a question for the Technical Infrastructure (Tech Infra) team - the engineers who run the cloud platforms, databases, developer tooling, and AI infrastructure behind Grab's ecosystem. Instead of immediately paging an on-call engineer, a bot first triages the request, checks the team's documentation, runbooks, and past Slack threads, and tries to answer directly in the thread. If it still cannot resolve the issue, it routes the ticket to the right human, with the relevant context already attached. That is what we built with the Tech Infra Support Bot. In the first half of 2023, Tech Infra handled thousands of support tickets, many of them repeated questions that had already been answered somewhere internally. Before LLMs, the bot's role was mainly operational; performing tasks like helping track acknowledgments and response times for on-call engineers. With the arrival of GPT-4-32k, we evolved it into a GPT-powered Level-0 support layer th