7 Steps to Mastering Memory in Agentic AI Systems
Memory is one of the most overlooked parts of agentic system design.
Why Agents Fail: The Role of Seed Values and Temperature in Agentic Loops
In the modern AI landscape, an agent loop is a cyclic, repeatable, and continuous process whereby an entity called an AI agent — with a certain degree of autonomy — works toward a goal.
7 Readability Features for Your Next Machine Learning Model
Unlike fully structured tabular data, preparing text data for machine learning models typically entails tasks like tokenization, embeddings, or sentiment analysis.
5 Production Scaling Challenges for Agentic AI in 2026
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Identifying Interactions at Scale for LLMs
Understanding the behavior of complex machine learning systems, particularly Large Language Models (LLMs), is a critical challenge in modern artificial intelligence. Interpretability research aims to make the decision-making process more transparent to model builders and impacted humans, a step toward safer and more trustworthy AI. To gain a comprehensive understanding, we can analyze these systems …
Information-Driven Design of Imaging Systems
An encoder (optical system) maps objects to noiseless images, which noise corrupts into measurements. Our information estimator uses only these noisy measurements and a noise model to quantify how well measurements distinguish objects. Many imaging systems produce measurements that humans never see or cannot interpret directly. Your smartphone processes raw sensor data through algorithms before …
AI-powered robot learns how to harvest tomatoes more efficiently
A new tomato-picking robot is learning to think before it acts. Instead of simply identifying ripe fruit, it predicts how easy each tomato will be to harvest and adjusts its approach accordingly. This smarter strategy boosted success rates to 81%, with the robot even switching angles when needed. The breakthrough could pave the way for …
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Trained on Tokens, Calibrated on Concepts: The Emergence of Semantic Calibration in LLMs
Large Language Models (LLMs) often lack meaningful confidence estimates for their outputs. While base LLMs are known to exhibit next-token calibration, it remains unclear whether they can assess confidence in the actual meaning of their responses beyond the token level. We find that, when using a certain sampling-based notion of semantic calibration, base LLMs are …
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Manufacturing with the Connected Edge
Industrial and defense environments generate massive amounts of data that can’t wait for the cloud. Latency is often measured in milliseconds, and resiliency is paramount. A manufacturing plant can’t go down due to flaky Wi-Fi or a public cloud outage. “Traditional” approaches — shipping servers, hiring local IT, bespoke development, managing one-off deployments — simply don’t scale. Critical operations …
Scaling Global Storytelling: Modernizing Localization Analytics at Netflix
Valentin Geffrier, Tanguy Cornuau Each year, we bring the Analytics Engineering community together for an Analytics Summit — a multi-day internal conference to share analytical deliverables across Netflix, discuss analytic practice, and build relationships within the community. This post is one of several topics presented at the Summit highlighting the breadth and impact of Analytics work across different …
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