Category: Strands Agents

In this post, we show you how Verizon Connect built and scaled an agentic AI solution to transform overwhelming fleet data into clear, actionable insights for 100,000 users daily. We walk you through the architectural decisions, implementation challenges, and measurable results that can guide your own data-to-insights transformation.

In this post you’ll learn how to build a multi-agent campaign review system that demonstrates parallel reasoning, context persistence, and traceable execution paths using an integrated architecture that combines NVIDIA NIM for GPU-accelerated inference. Amazon Bedrock AgentCore provides managed runtime, shared memory and built-in observability and Strands Agents provide serverless multi-agent orchestration. This approach supports performance, scalability, and operational insight in production environments. While the example focuses on marketing content review, the same pattern applies to digital assistants, review automation, and retrieval-augmented generation pipelines.

Building an AI app shouldn’t require a PhD in machine learning (ML) or months of wrestling with complex architectures. Yet that’s exactly what happens when you try to orchestrate multiple API calls, manage conversation state, and create agents that can reason on their own. I’ve seen straightforward AI ideas balloon into sprawling projects that demand […]

This solution combines the power of Amazon Bedrock AgentCore, Strands Agents, and Amazon Quick transforms to deliver a secure, scalable, and intelligent system for building and operating AI agents while transforming data into actionable business insights.

If you’re building visual shopping, image or document understanding, or chart analysis, you need a way to verify whether your model’s response is actually grounded in the source image. A text-only evaluator cannot tell you whether a caption faithfully describes an image, whether an extracted invoice total matches the document, or whether a screen summary […]

In this post, we’ll show you how our multi-document discovery feature solves this problem. It serves as an automated pre-processing step, analyzing unknown documents, clustering them by type, and generating schemas ready for the IDP Accelerator. You’ll learn how the new capability uses visual embeddings for automatic clustering and agents for schema generation. We’ll also walk you through running the solution on your own document collections.

In this post, you will learn how to set up the Exa integration in Strands Agents, understand the two core tools it exposes, and walk through real-world use cases that show how agents use web search to complete multi-step tasks.

In this post, we explore what it takes to migrate a traditional text agent into a conversational voice assistant using Amazon Nova 2 Sonic. We compare text and voice agent requirements, highlight design priorities for different use cases, break down agent architecture, and address common concerns like tools and sub-agents for reuse and system prompt adaptation. This post helps you navigate the migration process and avoid common pitfalls.

In this post, we demonstrate how to build AI agents using Strands Agents SDK with models deployed on SageMaker AI endpoints. You will learn how to deploy foundation models from SageMaker JumpStart, integrate them with Strands Agents, and establish production-grade observability using SageMaker Serverless MLflow for agent tracing. We also cover how to implement A/B testing across multiple model variants and evaluate agent performance using MLflow metrics and show how you can build, deploy, and continuously improve AI agents on infrastructure you control.

You can use ToolSimulator, an LLM-powered tool simulation framework within Strands Evals, to thoroughly and safely test AI agents that rely on external tools, at scale. Instead of risking live API calls that expose personally identifiable information (PII), trigger unintended actions, or settling for static mocks that break with multi-turn workflows, you can use ToolSimulator’s large language model (LLM)-powered simulations to validate your agents. Available today as part of the Strands Evals Software Development Kit (SDK), ToolSimulator helps you catch integration bugs early, test edge cases comprehensively, and ship production-ready agents with confidence.