FAANG - Robotic Content

Connect an AgentCore Runtime hosted MCP server to Amazon Quick

by AI Generated Robotic ContentFAANG September 1, 2026Comments are Disabled

Model Context Protocol (MCP) servers allow foundation models to access external data and tools, supporting standardized, secure access to files, databases, and APIs. They give AI agents the ability to interact with real-world applications, reduce hallucinations with accurate context, and offer stateful, multi-turn capabilities. Industry-standard architectures quickly evolved and adopted MCP to power agentic AI …

LLMs Are Not (Consistently) Bayesian: Quantifying Internal (In)consistencies of LLMs’ Probabilistic Beliefs

by AI Generated Robotic ContentFAANG August 29, 2026Comments are Disabled

Modern AI systems are being deployed in complex domains such as medicine, science, and law, where there is often not a single correct answer given the observed evidence. Such systems must be able to represent and update uncertain beliefs about the world as new evidence arrives to make rational decisions. We introduce the novel technique …

MAPS: Netflix’s Multimodal Asset Personalization at Scale

by AI Generated Robotic ContentFAANG August 29, 2026Comments are Disabled

By Emma Yanyang Kong, Aditya Deshpande, Asad Abbasi, Bowei Yan, David Fagnan, Ashish Rastogi, Dhaval Patel, Ray Zhang Introduction The Netflix experience is a journey of discovery. Every visual cue, from the artwork on a title to the video previews that autoplay while you browse, is there to connect you with a story you will love. …

Batch write and discover records in Amazon SageMaker Feature Store

by AI Generated Robotic ContentFAANG August 29, 2026Comments are Disabled

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. It provides low-latency online serving for real-time inference, an offline store for historical retention and training feature data, and supports both streaming and batch ingestion patterns. As ML platforms mature, two operational gaps surface …

From Preferences to Principles: Rubric-Based Alignment for Grounded Knowledge Answers

by AI Generated Robotic ContentFAANG August 28, 2026Comments are Disabled

Designing effective reward signals for open-domain question answering is challenging because high-quality responses must simultaneously satisfy multiple aspects of answer quality that are difficult to capture with a holistic scalar objective. We introduce a rubric-based reward framework that generates query-specific rubrics grounded in retrieved evidence and decomposed into multiple quality dimensions, providing fine-grained supervision during …

Build agentic creative workflows with Amazon Quick and fal

by AI Generated Robotic ContentFAANG August 28, 2026Comments are Disabled

Creative teams face growing demand for more assets, formats, and revisions, while their scripts, references, models, and outputs often remain fragmented across tools. Creators must repeatedly transfer context and assemble results manually. With 78% of creative leaders saying demand exceeds their teams’ capacity, faster generation alone does not solve the underlying workflow problem. To address …

Reimagining work: How Pythian’s internal AI playbook delivers customer ROI

by AI Generated Robotic ContentFAANG August 28, 2026Comments are Disabled

When Pythian rolled out Google Cloud’s Gemini Enterprise across our 500-person company in 27 countries, the goal was simple: use our own company as a proving ground to discover how enterprise AI actually delivers ROI. What we found changed our strategy entirely. Since the rollout of Gemini Enterprise and our previous enterprise AI deployments, Pythian …

PROOF-Gen: From Optimized Data to Better Distillation

by AI Generated Robotic ContentFAANG August 27, 2026Comments are Disabled

Supervised fine-tuning on teacher-generated trajectories is the standard first stage for distilling tool-calling capabilities into deployable models. Post-training pipelines that drive shipped tool-calling agents re-run this stage on a daily or weekly cadence, paying the frontier-teacher cost each cycle, yet the mechanism is generate-and-filter (keep the teacher’s passing trajectories, discard the rest) and each cycle …

Evaluate any agent framework with Amazon Bedrock AgentCore Evaluations

by AI Generated Robotic ContentFAANG August 27, 2026Comments are Disabled

AI teams building production agents face a frustrating asymmetry: the diversity of agent frameworks keeps growing, but evaluation tooling has not kept pace. Most evaluation systems assume you built your agent in a specific way: a specific SDK, a specific large language model (LLM) client, a specific tracing pattern. The moment you step outside that …

FinOps for the AI era: New flexible billing and cost controls for agents

by AI Generated Robotic ContentFAANG August 27, 2026Comments are Disabled

Editor’s note: A product image was updated after initial publication. As AI takes on more complex work, business leaders face a new challenge: enabling rapid innovation using agents while protecting their margins and budgets. To get a real return on AI, financial operations (FinOps) and cost management must evolve alongside technology, giving you clear visibility, …