Amazon.com, Inc. ( AM-ə-zon) is an American multinational technology company based in Seattle, Washington, which focuses on e-commerce, cloud computing, digital streaming, and artificial intelligence. It is considered one of the Big Four companies in the U.S. information technology industry, along with Google, Apple, and Facebook. The company has been referred to as "one of the most influential economic and cultural forces in the world", as well as the world's most valuable brand.Amazon was founded by Jeff Bezos in Bellevue, Washington, on July 5, 1994. The company started as an online marketplace for books but expanded to sell electronics, software, video games, apparel, furniture, food, toys, and jewelry. In 2015, Amazon surpassed Walmart as the most valuable retailer in the United States by market capitalization. In 2017, Amazon acquired Whole Foods Market for US$13.4 billion, which substantially increased its footprint as a physical retailer. In 2018, Bezos announced that its two-day delivery service, Amazon Prime, had surpassed 100 million subscribers worldwide.Amazon is known for its disruption of well-established industries through technological innovation and mass scale. It is the world's largest online marketplace, AI assistant provider, live-streaming platform and cloud computing platform as measured by revenue and market capitalization. Amazon is the largest Internet company by revenue in the world. It is the second largest private employer in the United States and one of the world's most valuable companies. Amazon distributes downloads and streaming of video, music, and audiobooks through its Prime Video, Amazon Music, Twitch, and Audible subsidiaries. Amazon also has a publishing arm, Amazon Publishing, a film and television studio, Amazon Studios, and a cloud computing subsidiary, Amazon Web Services. It produces consumer electronics including Kindle e-readers, Fire tablets, Fire TV, and Echo devices. Its acquisitions over the years include Ring, Twitch, Whole Foods Market, and IMDb. The company has been criticized for various practices including technological surveillance overreach, a hyper-competitive and demanding work culture, tax avoidance, and for being anti-competitive.
AI agents may soon become capable of autonomously completing valuable, long-horizon tasks in diverse domains. Current benchmarks either do not measure real-world tasks, or are not sufficiently difficult to meaningfully measure frontier models. To this end, we present Terminal-Bench 2.0: a carefully curated hard benchmark composed of 89 tasks in computer terminal environments inspired by problems from real workflows. Each task features a unique environment, human-written solution, and comprehensive tests for verification. We show that frontier models and agents score less than 65% on the benchmark and conduct an error analysis to identify areas for model and agent improvement. We publish the dataset and evaluation harness to assist developers and researchers in future work at tbench.ai.
Agent Skills are structured packages of procedural knowledge that augment large language model (LLM) agents at inference time. Despite rapid adoption, there is no standard way to measure whether they actually help. We present SkillsBench, a benchmark whose current inventory contains 87 tasks across 8 domains paired with curated Skills and deterministic verifiers. Our latest aggregate evaluation runs the 87-task benchmark under matched no-Skills and curated-Skills conditions for 18 model-harness configurations. Curated Skills raise the average pass rate from 33.9
We argue that reputation mechanisms used by platform markets suffer from two problems. First, buyers may draw conclusions about the quality of the platform from single transactions, causing a reputational externality across sellers. Second, for a variety of reasons we discuss, reputations will be biased. We document these problems using eBay data and claim that platforms can benefit from identifying and promoting higher quality sellers. We create an unobservable measure of seller quality and demonstrate the benefits of our approach through a controlled experiment that prioritizes better quality sellers. We highlight the importance of reputational externalities and chart an agenda that aims to create more realistic models of platform markets.
Reasoning is a fundamental cognitive process underlying inference, problem-solving, and decision-making. While large language models (LLMs) demonstrate strong reasoning capabilities in closed-world settings, they struggle in open-ended and dynamic environments. Agentic reasoning marks a paradigm shift by reframing LLMs as autonomous agents that plan, act, and learn through continual interaction. In this survey, we organize agentic reasoning along three complementary dimensions. First, we characterize environmental dynamics through three layers: foundational agentic reasoning, which establishes core single-agent capabilities including planning, tool use, and search in stable environments; self-evolving agentic reasoning, which studies how agents refine these capabilities through feedback, memory, and adaptation; and collective multi-agent reasoning, which extends intelligence to collaborative settings involving coordination, knowledge sharing, and shared goals. Across these layers, we distinguish in-context reasoning, which scales test-time interaction through structured orchestration, from post-training reasoning, which optimizes behaviors via reinforcement learning and supervised fine-tuning. We further review representative agentic reasoning frameworks across real-world applications and benchmarks, including science, robotics, healthcare, autonomous research, and mathematics. This survey synthesizes agentic reasoning methods into a unified roadmap bridging thought and action, and outlines open challenges and future directions, including personalization, long-horizon interaction, world modeling, scalable multi-agent training, and governance for real-world deployment.
The Model Context Protocol (MCP), introduced by Anthropic, provides a standardized framework for artificial intelligence (AI) systems to interact with external data sources and tools in real-time. While MCP offers significant advantages for AI integration and capability extension, it introduces novel security challenges that demand rigorous analysis and mitigation. This paper builds upon foundational research into MCP architecture and preliminary security assessments to deliver enterprise-grade mitigation frameworks and detailed technical implementation strategies. Through systematic threat modeling and analysis of MCP implementations and analysis of potential attack vectors, including sophisticated threats like tool poisoning, we present actionable security patterns tailored for MCP implementers and adopters. The primary contribution of this research lies in translating theoretical security concerns into a practical, implementable framework with actionable controls, thereby providing essential guidance for the secure enterprise adoption and governance of integrated AI systems.
