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Protein researchers face a time-consuming challenge: manually searching through thousands of peptide sequences to find structurally similar candidates is slow, error-prone, and requires deep domain expertise to interpret results. Building a protein research copilot can transform how researchers search for structurally similar peptides across large datasets — enabling natural language queries, automated embedding generation, and …

Build a protein research copilot with Amazon Bedrock AgentCore Read More »

Building multi-tenant AI applications presents new architectural challenges. You need complete tenant isolation between customers, different service tiers with different capabilities, granular cost tracking, and observability per tenant. Without these, you could risk exposing customer data, not providing appropriate quality of service to your customers or running up unforeseen costs. In this post, you will …

Shared infrastructure, isolated tenants: Pool model multi-tenancy with Amazon Bedrock AgentCore Read More »

This post was co-written with Kevin Jones from Ampersend (Edge & Node) and Chethan Shriyan from the Amazon Bedrock AgentCore Payments team. Ampersend and Amazon Bedrock AgentCore Payments are addressing one of the hardest problems in agentic AI. How do autonomous agents pay for services without developers building bespoke billing integrations, credential management, and payment …

Building pay-per-intelligence for AI agents: How Ampersend uses Amazon Bedrock AgentCore Payments Read More »

Turning a library of aerial imagery into a natural-language-searchable knowledge base is a problem that touches every industry that relies on geospatial data — insurance, real estate, government, infrastructure, and agriculture. The traditional path requires either manual tile-by-tile inspection or training a bespoke computer vision model for each new question. Multimodal embeddings, large language model …

Embed the world: Multimodal AI for searchable aerial imagery at scale Read More »

With ComfyUI workflows on Amazon SageMaker AI processing jobs, you can automate content generation at scale. For enterprises, every delay or misstep in creating compelling multimedia assets can mean lost sales, faded brand relevance, or missed marketing deadlines. When a product launch deadline looms or a seasonal promotion needs urgent assets, waiting for designers to …

Running ComfyUI workflows on Amazon SageMaker AI processing jobs Read More »

AI agents are changing how organizations find and act on information, but they share one structural limitation: their knowledge is frozen at training time. When you ask an agent that relies only on its training data about today’s stock price, a sports score, or a release that shipped an hour ago, it can’t respond. Web …

Introducing Web Search on Amazon Bedrock AgentCore Read More »

Amazon Quick and Adobe Marketing Agent help marketing teams access campaign insights within governed conversations in seconds. Marketers can ask questions about campaign performance, audiences, journeys, campaign conflicts, and content performance in natural language. Amazon Quick provides the chat experience and action orchestration. Adobe provides marketing-domain analysis to the approved data sources behind those questions. …

Accelerate campaign workflow with insights from Adobe Marketing Agent for Amazon Quick Read More »

Monitoring and troubleshooting generative AI inference endpoints operating at scale is challenging. When your large language model (LLM) endpoint’s P99 latency spikes, you must determine in minutes whether the root cause is GPU memory pressure, a saturated KV cache, unbalanced traffic across Availability Zones, or an auto scaling policy that hasn’t triggered. The shift from …

Monitor and debug generative AI inference with SageMaker detailed metrics and Insights dashboard on CloudWatch Read More »

A year ago, Simon Willison wrote one of the cleanest definitions of an agent that has stuck around: An LLM agent runs tools in a loop to achieve a goal. That definition stuck because it describes what every production agent actually does. Kiro, Amazon Q Developer, Quick Agents, Codex, Claude Code: under the hood, they …

Amazon Bedrock AgentCore harness is now generally available: Go from idea to production-grade agent in minutes Read More »

Today, we’re announcing inline payload support for Amazon SageMaker AI Async Inference. Customers can now send inference payloads directly in the request body of the InvokeEndpointAsync API, removing the need to upload input data to Amazon Simple Storage Service (Amazon S3) before each invocation. For payloads up to 128,000 bytes, this removes an entire network …

Amazon SageMaker AI Async Inference now supports inline request payloads Read More »