Artificial Intelligence

Category: Amazon Bedrock

Build a semantic layer for agentic AI on AWS with AtScale and Amazon Bedrock AgentCore

Build a semantic layer for agentic AI on AWS with AtScale and Amazon Bedrock AgentCore

The third post in our semantic-layer series builds a governed customer 360 model on Amazon Redshift with AtScale, then exposes it to a Strands agent on Amazon Bedrock AgentCore through the AtScale MCP server. Identity propagation, RBAC, row security, and full query audit are enforced at the semantic layer, not the prompt.

Structured outputs for Anthropic Claude models on Amazon Bedrock

Structured outputs for Anthropic Claude models on Amazon Bedrock

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.

Pegasus 1.5 from TwelveLabs is now available in Amazon Bedrock

Pegasus 1.5 from TwelveLabs is now available in Amazon Bedrock

TwelveLabs Pegasus 1.5 is now available in Amazon Bedrock. The video language model turns long-form video into structured, timestamped metadata: define the moments that matter and get every matching segment back as JSON, with new video segmentation, multimodal prompting with reference images, and on-the-fly processing of up to two hours of video.

Building trust in AI agents: A production-ready evaluation framework with Amazon Bedrock AgentCore

Building trust in AI agents: A production-ready evaluation framework with Amazon Bedrock AgentCore

Autonomous AI agents fail softly: latency stays flat while answer quality quietly erodes. This post shares a nine-stage evaluation framework, built on Amazon Bedrock AgentCore, that catches silent quality regressions across the full agent lifecycle, from first commit to live production.

Build self-improving document processing using Amazon Bedrock AgentCore

Build self-improving document processing using Amazon Bedrock AgentCore

Learn how to build an agentic intelligent document processing (IDP) system using Amazon Bedrock AgentCore that classifies, extracts, validates, and learns from business documents with minimal human intervention, automatically handling new document formats without manual template configuration.

How Tamnoon built AI-powered cloud security remediation with Amazon Bedrock

How Tamnoon built AI-powered cloud security remediation with Amazon Bedrock

Learn how Tamnoon built Tami, an agentic cloud remediation orchestrator on Amazon Bedrock that autonomously investigates and safely remediates cloud security findings from CNAPP and cloud-native sources. The approach drove a 90%+ reduction in critical cloud exposure in 90 days with zero production issues.

Building agents that transact with Anchor Browser and AgentCore payments

Building agents that transact with Anchor Browser and AgentCore payments

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.

Manage private pricing offers for Amazon Bedrock with managed entitlements

Manage private pricing offers for Amazon Bedrock with managed entitlements

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.

Natural language security queries at Orca Security using Amazon Bedrock

Natural language security queries at Orca Security using Amazon Bedrock

Orca Security built Discovery AI, a natural language query agent on Amazon Bedrock that converts plain-English questions into Sonar queries. The post shares the multi-stage architecture and the results: 90% lower query cost, 93% fewer input tokens, and 75% lower latency than the initial baseline.