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Introducing agent-to-agent protocol support in Amazon Bedrock AgentCore Runtime

We recently announced the support for Agent-to-Agent (A2A) protocol on Amazon Bedrock AgentCore Runtime. With this addition, agents can discover peers, share capabilities, and coordinate actions across platforms using standardized communication. Amazon Bedrock AgentCore Runtime provides a secure, serverless environment designed for deploying AI agents and tools. It works with any framework and model, supports …

BigQuery under the hood: How Google brought embeddings to analytics

Embeddings are a crucial component at the intersection of data and AI. As data structures, they encode the inherent meaning of the data they represent, and their significance becomes apparent when they are compared to one another. Vector search is a technique that uncovers the relative meaning of those embeddings by evaluating the distances between …

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Fine-tune VLMs for multipage document-to-JSON with SageMaker AI and SWIFT

Extracting structured data from documents like invoices, receipts, and forms is a persistent business challenge. Variations in format, layout, language, and vendor make standardization difficult, and manual data entry is slow, error-prone, and unscalable. Traditional optical character recognition (OCR) and rule-based systems often fall short in handling this complexity. For instance, a regional bank might …

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Running high-scale reinforcement learning (RL) for LLMs on GKE

As Large Language Models (LLMs) evolve, Reinforcement Learning (RL) is becoming the crucial technique for aligning powerful models with human preferences and complex task objectives. However, enterprises that need to implement and scale RL for LLMs are facing infrastructure challenges. The primary hurdles include the memory contention from concurrently hosting multiple large models (such as …

ExpertLens: Activation Steering Features Are Highly Interpretable

This paper was accepted at the Workshop on Unifying Representations in Neural Models (UniReps) at NeurIPS 2025. Activation steering methods in large language models (LLMs) have emerged as an effective way to perform targeted updates to enhance generated language without requiring large amounts of adaptation data. We ask whether the features discovered by activation steering …

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Connect Amazon Bedrock agents to cross-account knowledge bases

Organizations need seamless access to their structured data repositories to power intelligent AI agents. However, when these resources span multiple AWS accounts integration challenges can arise. This post explores a practical solution for connecting Amazon Bedrock agents to knowledge bases in Amazon Redshift clusters residing in different AWS accounts. The challenge Organizations that build AI …

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Easy AI workflow automation: Deploy n8n on Cloud Run

n8n is a powerful yet easy-to-use workflow and automation tool for multi-step AI agents, and many teams want a simple, scalable, and cost-effective way to self-host it. With just a few commands, you can deploy n8n to Cloud Run and have it up and running, ready to supercharge your business with AI workflows that can …

How does AI work?

TL;DR: Artificial Intelligence learns patterns from data and uses them to make predictions, generate content, or solve problems. Generative AI, such as ChatGPT or image and video generators, takes this a step further by creating new things, text, art, music, and more, that have never existed before. People often ask: “How does AI actually work?” …

PolyNorm: Few-Shot LLM-Based Text Normalization for Text-to-Speech

Text Normalization (TN) is a key preprocessing step in Text-to-Speech (TTS) systems, converting written forms into their canonical spoken equivalents. Traditional TN systems can exhibit high accuracy, but involve substantial engineering effort, are difficult to scale, and pose challenges to language coverage, particularly in low-resource settings. We propose PolyNorm, a prompt-based approach to TN using …

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Transform your MCP architecture: Unite MCP servers through AgentCore Gateway

As AI agents are adopted at scale, developer teams can create dozens to hundreds of specialized Model Context Protocol (MCP) servers, tailored for specific agent use case and domain, organization functions or teams. Organizations also need to integrate their own existing MCP servers or open source MCP servers for their AI workflows. There is a …