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  1. 01Zalando · architecture

    Automating Data Ingestion with a RAG Flywheel

    Zalando replaced manual schema mapping for diverse supplier data with an LLM-powered Retrieval-Augmented Generation (RAG) flywheel. The system combines Amazon Titan embeddings stored in PostgreSQL via pgvector with tool-constrained generative models running at zero temperature. Every LLM suggestion passes through strict API validation checks before being reviewed by operations specialists, creating a continuous learning loop that improves future predictions.

    DEPTH ■■■■■14 min read2 weeks ago

    Machine LearningPostgreSQL

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  2. 02Zalando · postmortem

    Thundering herd of circuit breakers

    An engineering team investigated why a client-side circuit breaker repeatedly failed to close on its own after an outage. By exporting OpenTelemetry traces into a custom visualization tool, they discovered that low probe traffic caused the autoscaler to scale down the service to minimum capacity, triggering a thundering herd of requests and immediate timeouts the moment the circuit breaker closed. Adjusting the system to account for capacity and probe representativeness prevents this oscillation.

    DEPTH ■■■■■10 min read2 weeks ago

    MicroservicesObservabilityReliability

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  3. 03Stack Overflow · practice

    Taking a look under your agent’s hood

    This post discusses the challenges and methods of applying observability to AI agents, which combine traditional distributed systems telemetry with new requirements for monitoring LLMs and multi-step tool calls. The discussion highlights how non-deterministic agent behavior and increased speed of execution make continuous production feedback loops essential for upstream development. Ultimately, integrating observability data into automated testing and development loops helps teams iterate on agent performance.

    DEPTH ■■■■■25 min read2 weeks ago

    Machine LearningObservability

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