All news with #amazon bedrock tag

🔍 Amazon Connect Customer now provides AI agent traces for self-service voice interactions, letting operators inspect how AI agents reasoned, acted, and responded during conversations. The feature displays step-by-step traces alongside full transcripts in the Connect web UI so teams can confirm correct behavior, diagnose failures, or spot tool and parameter issues. It is available in all AWS Regions that support Amazon Connect Customer AI Agents and is documented in the Amazon Connect Customer Administrator Guide.

🖥️ Amazon Bedrock AgentCore Runtime introduces the InvokeAgentRuntimeCommandShell API, providing a persistent, PTY-backed terminal over WebSocket into running agent sessions. This complements existing one-shot execution via InvokeAgentRuntimeCommand and delivers a full terminal experience inside an isolated microVM with features like colors, tab completion, Ctrl+C, resize, and automatic reconnect. Developers hosting coding agents (for example, Claude Code, OpenAI Codex, Amazon Kiro) can now authenticate, drop into the agent microVM, inspect files, run ad-hoc commands, and debug while retaining session state across reconnects. Each interactive session uses a runtime session ID and shell ID for resume; up to 10 concurrent shells are supported per runtime.

🛠️ The Amazon Bedrock console has been redesigned to match real-world model development workflows: experiment, iterate, and scale. The refreshed UI centers on the bedrock-mantle endpoint and is compatible with the OpenAI Responses API, OpenAI Chat Completions API, and the Anthropic Messages API. Users can browse and compare models, create projects to run evaluations, and get project-aware code snippets prefilled with model ID, region, endpoint URL, and API key references. The new experience is available in all Regions where the bedrock-mantle endpoint is offered.

🤖 AWS Step Functions now integrates with the Amazon Bedrock AgentCore managed harness (preview) to add AI agent reasoning steps to workflows. The integration lets you declare agents via configuration, run agents in parallel or sequence, add human approvals, and view execution history with agent inputs, outputs, token usage, and CloudWatch links. You can reuse or create harnesses from Workflow Studio, apply per-invocation overrides, and persist agent context with session IDs. The harness preview and integration are available in select regions and standard Step Functions and Bedrock pricing applies.

🛡️ Amazon Bedrock now offers OpenAI GPT‑5.4 in AWS GovCloud (US‑West), enabling government and regulated industry customers to use OpenAI's most capable frontier model with the security and compliance of GovCloud. GPT‑5.4 delivers native computer-use capabilities and advanced reasoning across coding, documents, and multi-step agentic tasks, running on Bedrock's high-performance inference engine. Data remains in-partition and is not used to train models.

🔒 This post shows how SaaS providers can use Amazon Bedrock AgentCore resource-based policies to control multi-tenant access to a shared AgentCore Runtime and Runtime endpoint. It walks through two tenant scenarios: cross-account access for Example Corp and VPC-restricted access for AnyCompany, demonstrating how to apply resource-level Allow and explicit Deny conditions. The article covers required IAM permissions, example policy files, and verification steps to ensure network- and identity-based constraints are enforced.

🔒 Amazon Quick now supports connecting privately hosted Model Context Protocol (MCP) servers via Amazon Virtual Private Cloud (VPC). This enables organizations to integrate proprietary MCP servers running on Amazon EC2, AWS Fargate, AWS Agentcore, or other private compute without exposing them to the public internet. During connector creation, choose your VPC and provide your MCP server URL so teams can interact with private MCPs in Quick while traffic remains routed securely through the VPC.

🔐 Amazon Bedrock AgentCore Identity now lets customers reference existing AWS Secrets Manager secret ARNs directly in Credential Providers. Previously, secrets were service-managed and created by AgentCore Identity, limiting tagging, CMK encryption, and governance controls. Customers can now create and manage secrets with their own policies and then reference the ARN without changing runtime behavior. This feature is GA in 14 AWS Regions.

🟦 Amazon Bedrock customers can now monitor inference traffic to the bedrock-mantle endpoint using Amazon CloudWatch metrics, matching existing support for the bedrock-runtime endpoint and other AWS services. The bedrock-mantle endpoint supports OpenAI Responses and Chat Completions APIs as well as the Anthropic Messages API, enabling easy migration of OpenAI- or Anthropic-based applications to Bedrock. Metrics are published under the AWS/BedrockMantle namespace and include inference counts, token totals, and client error counts across account, project, model, and project-and-model granularities. These metrics are available in all Regions where the bedrock-mantle endpoint is offered and can be used to monitor production inference, set alarms, and plan capacity.

🚀 Amazon Bedrock now supports OpenAI GPT-5.5, GPT-5.4, and Codex for production use, offering the same AWS security, governance, and operational controls. GPT-5.5 delivers advanced capabilities for agentic coding, data analysis, and multi-step autonomous tasks on a next-generation inference engine. Codex is available via a dedicated App, CLI, and IDE integrations for Visual Studio Code, JetBrains, and Xcode, and can be configured to run through Bedrock with pricing aligned to OpenAI first-party rates.

🤖 AWS now offers Claude Opus 4.8, Anthropic's most capable generally available model, bringing advances in agentic coding, professional knowledge work, and autonomous long-running tasks for developers and enterprises. The model sustains longer sessions, reasons more deeply, and maintains consistency for production workflows. Customers can access Opus 4.8 via Amazon Bedrock or the Claude Platform on AWS, with AWS-managed features and unified billing.

🚀 Amazon Bedrock customers can now view inference quotas for the bedrock-mantle endpoint via AWS Service Quotas. This update surfaces per-model input-tokens-per-minute and output-tokens-per-minute limits, aligning visibility with existing bedrock-runtime and other AWS services. The feature is available in all Regions offering the endpoint and quota increases follow the standard Bedrock limit request process.

🚀 AWS announces Amazon Aurora MySQL-Compatible Edition now integrates with Kiro Powers, enabling developers to build Aurora MySQL-backed applications faster using AI agent assistance. The integration bundles curated Model Context Protocol (MCP) servers, steering files, and hooks validated by Kiro partners to provide immediate expertise in Aurora MySQL operations and schema design via natural language. Developers can execute data plane and control plane tasks conversationally, while task-specific guidance prevents information overload. The feature is available via one-click installation from the Kiro IDE and webpage across all Regions that support Aurora MySQL.

🛈 Amazon Bedrock now supports request-level usage attribution on the InvokeModel and InvokeModelWithResponseStream APIs, enabling customers to tag individual model inference calls with attributes such as team, project, and environment. This capability extends existing attribution options like application inference profiles, IAM principal attribution, project-level tracking on bedrock-mantle, and workspace tracking for Anthropic Claude models. Customers can enable model invocation logging in their AWS Region and include metadata in requests to analyze usage in Bedrock model invocation logs. The feature is available in all AWS commercial Regions where Amazon Bedrock is offered.

🔒 Amazon explains how AgentCore Gateway enforces a centralized authorization layer between autonomous agents and external tools, treating the LLM as an untrusted actor. Policies are expressed in the open-source Cedar language for readability, bounded execution, and mathematical analyzability, enabling deterministic enforcement and formal verification during policy authoring and attachment. A neuro-symbolic workflow translates natural-language rules into Cedar, validates them with Cedar Analysis, and enforces decisions at runtime to constrain tool invocations and filter unavailable actions.

💬 AWS announces that Partner Central agents let partners create sales opportunities via natural language conversation instead of multi-step forms. Released March 16, 2026 and built on Amazon Bedrock AgentCore, the agents ingest meeting notes, proposals, and transcripts (PDF, DOCX, Excel, TXT), extract details, and recommend improvements. Accessible through Amazon Q chat in the AWS Console and programmatically via Model Context Protocol (MCP), they aim to reduce data entry, improve pipeline hygiene, and shorten sales cycles across all commercial AWS Regions.

🔒 The AWS AI Security Framework presents a structured model that helps security and business leaders align the right controls to the right use case, at the right layer, and at the right phase so AI can move from prototype to production securely. Its core principle is that you build AI on top of security, not add security later. The post maps controls across three layers—infrastructure, identity and data, and AI application—and across four use cases from answering to agentic and physical AI. It highlights Amazon Bedrock and AgentCore as pillars that decouple model choice from security infrastructure.

🔒 The updated AWS User Guide to Governance, Risk, and Compliance for Responsible AI Adoption provides Financial Services customers practical GRC guidance for deploying AI responsibly. It covers governance, risk management, compliance, data and model management, and AI agent oversight, and maps these considerations to AWS capabilities. The guide highlights services such as Amazon Bedrock AgentCore, Bedrock Guardrails, Bedrock Agents, SageMaker Autopilot, and SageMaker Model Monitor. It complements existing AWS responsible AI and Well-Architected resources and is available on the AWS Whitepaper portal.

💳 Amazon Bedrock AgentCore now offers a preview of AgentCore payments, enabling AI agents to autonomously discover and pay for APIs, MCP servers, web content, and other agents. Built with Coinbase and Stripe, the feature manages wallet authentication, x402 protocol negotiation, stablecoin payment execution, and proof delivery without interrupting an agent's reasoning loop. Developers can attach a Coinbase CDP or Stripe Privy wallet, set session-level spending limits enforced at the infrastructure layer, and observe every transaction through AgentCore's existing logs, metrics, and traces. The Coinbase x402 Bazaar MCP server is accessible via AgentCore Gateway, and the preview is available in four AWS Regions.

🔌 Amazon announced that Bedrock AgentCore Runtime now supports bring-your-own file system mounts for Amazon S3 Files and Amazon EFS access points. Developers can attach these file systems into every agent session at a specified path so agents use standard file operations without custom mount code, privileged containers, or pre-run download orchestration. The feature preserves sub-millisecond latency for active data and NFS close-to-open consistency. It is available across the 15 AWS Regions that support AgentCore Runtime and requires an access point ARN plus a configured VPC.