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Grab's LLM-Kit framework significantly reduces the operational overhead of deploying AI agents by centralizing infrastructure management, including secrets handling, tracing, and runtime tool discovery via Model Context Protocol. This shift from per-service customization to a standardized scaffolding approach cuts production deployment time from weeks to approximately one hour without altering the core reasoning logic.

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Grab's Agent Framework LLM-Kit Accelerates AI Agent Production Deployment

InfoQSeptember 15, 20263 min read

Grab has implemented LLM-Kit, a framework that standardizes over 500 internal agent services. This system enhances service integration, evaluation, and secret handling, reducing the time to deploy new AI agents from two weeks to one hour. It centralizes infrastructure management, allowing runtime tool discovery and flexible model integration, while maintaining operational control. By Hien Luu

Grab has standardized more than 500 internal agent services on LLM-Kit, an internal framework of scaffolding and integrations that hands a new service a basic agent loop plus evaluation, tracing, secret handling, and tool-server connections already wired in. Agents discover their tools at runtime from over 50 MCP servers and reach models through a single gateway, so new capabilities and new…

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