Category: Intermediate (200)

U.S. Hunger, a nonprofit addressing food insecurity, uses Amazon Quick to connect organizational data with leadership decisions. Leaders explore applicant data directly, share KPI definitions and financial context in Quick Spaces, and investigate policy questions with Quick Research.

Canadian organizations can now access OpenAI GPT-5.6, GPT-6, and GPT-6.1 models on Amazon Bedrock from the Canada (Central) and Canada West (Calgary) Regions using cross-Region inference. This post covers the models and inference profiles, how to set up permissions, and how to invoke them with the Responses, Chat Completions, and Converse APIs.

Hardcoded tool wiring breaks down as an agent’s catalog grows from a handful of tools to hundreds. This post walks through a runtime Discover, Instantiate, Execute loop built on AWS Agent Registry, where an orchestrator discovers MCP servers and A2A agents through semantic search at runtime instead of compiling every tool at build time.

Amazon Bedrock now supports structured outputs for all Anthropic Claude models on both the bedrock-runtime and bedrock-mantle endpoints. Receive schema-compliant JSON that conforms to a defined structure, without custom parsing, retry logic, or post-processing, using JSON Schema output format and strict tool use.

Amazon SageMaker JumpStart onboarded over 100 new models from 30 providers in the first three quarters of 2026. This recap covers the model-onboarding highlights and JumpStart enhancements, including the simplified Model Hub, pre-configured deployment options, and Hugging Face deep link integration.

Muse Spark Open, Meta’s coding-optimized open-weight model, is now available in Amazon SageMaker JumpStart. Deploy it to a SageMaker AI endpoint in your own AWS account using SageMaker Studio or the SageMaker Python SDK, and run your first inference request.

Enterprise leaders want AI agents to take on more work but don’t fully trust them to act correctly every time. Trust in Depth is AWS’s layered approach-scope, know, bound, observe, and prove-that lets agents act on their own when their actions can be verified, reserving human judgment for the decisions that need it.

Learn how to deploy an available model from Amazon SageMaker JumpStart, run a lightweight and repeatable performance test using the SageMaker Python SDK, and delete the endpoint when testing is complete.

Learn how the AWS Generative AI Innovation Center Partner Agent Factory and Anchor Browser built AI agents that autonomously pay for x402-protected web resources and APIs. Amazon Bedrock AgentCore payments checks budgets, signs transactions, and retries requests without exposing wallet credentials to the agent runtime.

Managing AI model access across dozens or hundreds of AWS accounts creates friction when each account must accept AWS Marketplace Private Pricing Offers individually. This post shows two ways to activate MPPO entitlements in AWS License Manager: a lightweight script for a one-time fix, and CDK automation for ongoing event-driven processing.