Agentic Engineering at Zalando: a snapshot

While the landscape and environment rapidly changes every day, the whole industry is figuring out how to approach Agentic Engineering. With more than 250 engineering teams innovating across our business lines, we're observing value and impact of LLMs in different forms and at different paces. We shared some early wins on using LLMs for product data enrichments, LLM as a judge for improving search quality, relevance assessment in product search, or frontend migrations.

Recently, we have been looking back at our progress in the past 2.5 years and we'd like to share a few approaches that worked well for us.

LLM proxy for API-based LLM access from day 1¶

We've been a GitHub Copilot user from the early days when it offered autocomplete in the IDE. To complement this offering and provide API-based access to LLMs, our ML platform team deployed in January 2024 a LiteLLM based API proxy with access to models from different providers (now: OpenAI, AWS Bedrock, and Google Vertex). This way, it became easy for our engineers to experiment with different tools and models. The platform team got a single point to measure adoption via: MAU, WAU, model, User-Agent.