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Official AWS What's New (AI-filtered) & AWS ML Blog RSS + GitHub + Hugging Face.

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61 updates · page 1 of 5

  1. Friday, October 16, 2026

    Model Important

    Introducing Reflection Beam: an open-weight model for agentic coding on AWS

    Reflection AI introduced Beam, an open-weight model for agentic coding and long-horizon tasks such as maintaining code, using tools, interpreting results, and completing multi-step workflows.

    Why it matters: A new model option to evaluate — compare quality, price, and context length against what you use today.

    AWS Machine Learning BlogOfficial#open-source#agents#coding
  2. Feature

    Web Search on Amazon Bedrock AgentCore expands to AWS GovCloud and 3 Regions

    Web Search on Amazon Bedrock AgentCore is now in US East (Ohio), US West (Oregon), Asia Pacific (Sydney), and AWS GovCloud (US-West), giving agents current web grounding.

    Why it matters: Agents in those Regions can ground research, summaries, and answers with managed web search.

    AWS What's NewOfficial#agents
  3. Developer

    Strands Agents SDK python v1.61.1

    Strands Agents SDK python v1.61.1 drops reasoning blocks a target model rejects when history crosses model families, and surfaces a subagent's stop reason.

    Why it matters: Python Strands users can keep Bedrock history when switching model families without rejected reasoning blocks.

    GitHub · strands-agents/sdk-pythonOfficial#agents#reasoning
  4. Developer

    Strands Agents SDK harness-python v0.3.1

    Strands Agents SDK harness-python v0.3.1 includes the same Bedrock reasoning-block fix and surfaces a subagent's stop reason.

    Why it matters: Harness users on v0.3.1 get the cross-family Bedrock history fix and a clearer subagent stop reason.

    GitHub · strands-agents/sdk-pythonOfficial#agents#reasoning
  5. Model Important

    Muse Spark Open model now available on Amazon SageMaker JumpStart

    Meta's Muse Spark Open is now on Amazon SageMaker JumpStart, an open-weight coding model for agentic coding, multi-step reasoning, and tool calling on AWS.

    Why it matters: AWS customers can run Muse Spark Open on SageMaker JumpStart for long-running coding agents.

    AWS What's NewOfficial#open-source#agents#coding
  6. Thursday, October 15, 2026

    Developer

    Strands Agents SDK python v1.61.0

    Strands Agents SDK python v1.61.0 packages Strands skills for Claude, Codex, and Cursor, and adds a DecisionModel primitive and LLMDecisionModel.

    Why it matters: Python Strands users can install skills for Claude, Codex, and Cursor and use the new DecisionModel types in v1.61.0.

    GitHub · strands-agents/sdk-pythonOfficial#agents#coding
  7. Developer

    Strands Agents SDK harness-cli v0.2.1

    Strands Agents SDK harness-cli v0.2.1 packages Strands skills for Claude, Codex, and Cursor, and adds a DecisionModel primitive and LLMDecisionModel.

    Why it matters: CLI users on harness-cli v0.2.1 can install those skills and use the new DecisionModel types.

    GitHub · strands-agents/sdk-pythonOfficial#agents#coding
  8. Developer

    Strands Agents SDK harness-typescript v0.3.0

    Strands Agents SDK harness-typescript v0.3.0 packages Strands skills for Claude, Codex, and Cursor, and adds a DecisionModel primitive and LLMDecisionModel.

    Why it matters: TypeScript harness users can take the Claude, Codex, and Cursor skills plus DecisionModel in v0.3.0.

    GitHub · strands-agents/sdk-pythonOfficial#agents#coding
  9. Feature

    Amazon SageMaker Studio simplifies getting started for new AI/ML builders with social sign-in

    Amazon SageMaker Studio now lets new customers sign up with Google, GitHub, or Apple credentials and land in a fully configured Studio environment, with no traditional AWS account setup, credit card, or console navigation.

    Why it matters: New AI and ML builders can open a configured SageMaker Studio from a social login without a credit card or console setup.

    AWS What's NewOfficial
  10. Wednesday, October 14, 2026

    Feature

    Build self-improving document processing using Amazon Bedrock AgentCore

    A guide shows how to build self-improving document processing with Amazon Bedrock AgentCore. It cites document-related costs of $19,732 per information worker a year, a 21.3 percent productivity cut, and staff spending up to 30 percent of their time on documents.

    Why it matters: Teams receiving invoices from hundreds of suppliers can build AgentCore document processing instead of maintaining templates for every changing layout.

    AWS Machine Learning BlogOfficial#audio
  11. Developer

    Generate presentations with Amazon Bedrock AgentCore

    A walkthrough shows how to build an Amazon Bedrock AgentCore system that turns quarterly reports, compliance documents, and market analyses into client PowerPoint presentations.

    Why it matters: Financial services teams can automate slide creation from those reports instead of reading hundreds of pages by hand.

    AWS Machine Learning BlogOfficial#agents
  12. Feature

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

    Tamnoon built AI-powered cloud security remediation with Amazon Bedrock so findings from CNAPP tools, cloud providers, and posture scanners can move from detected to fixed.

    Why it matters: Security teams can use the Bedrock approach to investigate context, find the owner, and remediate cloud findings that otherwise sit untouched.

    AWS Machine Learning BlogOfficial#safety
  13. Developer

    Building agents that transact with Anchor Browser and AgentCore payments

    A walkthrough shows agents paying for web resources that return HTTP 402 Payment Required, using Anchor Browser and AgentCore payments.

    Why it matters: Developers can attach a payment path when an agent hits a dataset, API, or publisher that answers with HTTP 402.

    AWS Machine Learning BlogOfficial#agents
  14. Pricing

    Manage private pricing offers for Amazon Bedrock with managed entitlements

    Amazon Bedrock managed entitlements let a central account subscribe once to third-party model private pricing offers and distribute that access across an organization.

    Why it matters: Teams with many AWS accounts can stop accepting each marketplace private pricing offer in every workload account.

    AWS Machine Learning BlogOfficial