by Simha Sethumadhavan on Jun 2, 2026 | Tags: AI Agents, Security, Spectre
When an agent makes an incorrect guess, the obvious mistakes like bad files or stale outputs are straightforward to see. However, there are less visible leaks that pose significant risks, such as timing patterns or cached context. The context and data exchanged between tools, services, and third-party systems can also be problematic. This situation becomes particularly concerning when AI agents take action before fully understanding the task at hand. This leads to an important question: Who holds the responsibility for addressing the residue left behind by agentic mistakes?
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by Dimitrios Skarlatos on May 19, 2026 | Tags: AI Agents, Hardware-Software Co-design
Architecture & Systems are Changing: The Architect’s Role in the Era of Agentic Co-Design The AI datacenter stack is built on hardware-software contracts and abstractions that were never designed for the workloads datacenters now serve. Memory systems strain...
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by Shvetank Prakash and Vijay Janapa Reddi on Jan 7, 2025 | Tags: AI Agents, Benchmarks, Datasets, Machine Learning
Introduction The rise of large language models (LLMs) and generative artificial intelligence (GenAI) presents new opportunities to build innovative tools and is already enabling revolutionary AI-based tools in various domains. However, a significant gap remains in the...
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by Bryan Chin, Jishen Zhao, Haoxing (Mark) Ren, Stelios Diamantidis, Hans Bouwmeester, Hanxian Huang on Oct 16, 2024 | Tags: AI Agents, Chip Design, Large Language Models
New hardware capabilities have enabled transformational AI technologies in many industries and applications. One of those industries is hardware design itself, the very discipline that enabled increased AI capabilities in the first place. At Hotchips 2024, we held a...
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