Article: The Platform Engineering Playbook for Production LLMs
In this article, author discusses his experience with AI agent hallucinations in an inventory recommendation system and how this problem was solved by treating the LLM stack as a platform infrastructure concern instead of as an application one. He makes a case for a shared LLM platform with common services like prompt registry & versioning, schema enforcement and token cost attribution by…
The article discusses a platform engineering strategy for production Large Language Models (LLMs). Early experiments with an LLM-driven inventory recommendation system in production revealed that about 15% of agent responses contained hallucinations, or confident but incorrect outputs. After six months, this rate dropped to 1.5%, primarily due to a shift in perspective rather than an improvement in the foundation model.
Instead of treating the LLM stack as part of the application, the team started viewing it as platform infrastructure.
The platform was built by a team of business, product, software engineers, data scientists, and data engineering experts. It now supports several application teams dealing with inventory audits, replenishment review, discrepancy triage, and more. The article details the lessons learned and outlines three key properties of the platform: it eliminates application-level bugs, imposes a contract for shared resources, and streamlines the process of bringing new applications to production.
The platform architecture consists of a single gateway that handles authentication, role-based access, metrics, and admin prompt rollback. Behind the gateway, a root coordinator agent uses Google's Agent Development Kit to classify user intent and delegate tasks to specialist agents. These agents maintain their own tool connections, partitioned by team or usage, and enforce authorization policies. The platform uses LiteLLM to interface with various foundation models, making it easy to swap out providers.
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