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Stop Building AI Agents!

2025-07-03

Hugo, an expert advising teams at Netflix, Meta, and the US Air Force on building LLM-powered systems, argues that many teams prematurely adopt AI agents, resulting in complex, hard-to-debug systems. He contends that simpler workflows like chaining, parallel processing, routing, and orchestrator-worker patterns are often more effective than complex agents. Agents are only the right tool when dealing with dynamic workflows requiring memory, delegation, and planning. The author shares five LLM workflow patterns and emphasizes the importance of building observable and controllable systems. Avoid agents for stable enterprise systems; they are better suited for human-in-the-loop scenarios.

Offline vs. Online ML Pipelines: The Key to Scaling AI

2025-05-13

This article highlights the crucial difference between offline and online machine learning pipelines in building scalable AI systems. Offline pipelines handle batch processing, such as data collection, ETL, and model training, while online pipelines serve predictions in real-time or near real-time to users. The article stresses the importance of separating these pipelines and uses a feature pipeline for fine-tuning a summarization SLM as an example. It explains how to build a reproducible, trackable, and scalable dataset generation process using MLOps frameworks like ZenML. This process extracts data from MongoDB, processes it through various stages, and finally publishes it to Hugging Face. Understanding this separation is crucial for building robust, production-level AI systems.

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