All news with #bedrock agents tag

🔒 Amazon Bedrock AgentCore Memory now supports cross-account access, enabling multi-account architectures where memory resources and consuming agents span AWS accounts. Administrators can attach resource-based policies to memory resources to grant principals in other accounts permission to call memory data plane APIs by referencing the full memory ARN. Cross-account delivery destinations let memory resources stream payloads and events to Amazon S3, Amazon SNS, and Amazon Kinesis Data Streams in separate accounts. This capability is available in all Regions where AgentCore Memory is supported.

🔒 Check Point and AWS are collaborating to secure enterprise AI agents by integrating Amazon Bedrock AgentCore with Check Point AI Security. This partnership extends AgentCore’s identity, gateway, registry, and policy capabilities with runtime behavioral protections that monitor agent actions across models, tools, data, and applications. The integration enables policy-driven governance, visibility into agent deployments, and prevention of misuse such as prompt injection or unintended data exposure.

🚀 Amazon Bedrock AgentCore announces general availability of its managed agent harness, enabling teams to deploy production-grade agents in minutes. The harness handles orchestration, tool execution, session isolation, persistent memory, failure recovery, and context management so customers define agents via configuration rather than coding the loop. It supports any model, mid-session model switching, integrated security and observability, and exports to code for custom orchestration, and is available today in all AWS Commercial Regions where AgentCore is offered.

📌 Amazon Bedrock AgentCore Memory now allows applications to attach metadata values directly to short-term memory events so those values are preserved exactly when converted into long-term memory records. Previously, metadata had to be inferred by the LLM during extraction. Setting a metadata key to STRICTLY_CONSISTENT ensures the provided value is unchanged, prevents merging across different values, and lets you group events for department scoping, compliance boundaries, and multi-tenant isolation. The feature supports up to three strictly consistent STRING keys per strategy and is available in all AWS Regions where AgentCore Memory is supported.

🔎 Amazon Bedrock AgentCore now offers Web Search generally available in US East (N. Virginia). The fully managed tool provides agents with current web knowledge, combining an Amazon-operated web index and structured knowledge graph data to return high-value excerpts, entity data, and verified facts. Web Search integrates with AgentCore via the Model Context Protocol (MCP) and keeps data residency within your secured AWS environment with zero data egress.

🖥️ 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.

🤖 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 SageMaker AI introduces multi-turn reinforcement learning (RL), a serverless model customization method for fine-tuning models on multi-step, agentic tasks. The feature trains models against users' agent environments, rewarding entire decision sequences to improve task accuracy of smaller, cost‑effective models versus larger general-purpose models. It integrates with Amazon Bedrock AgentCore Runtime and other deployment targets, and handles rollout orchestration, trajectory collection, training, and checkpoints, with MLflow tracking and evaluation metrics. Multi-turn RL runs serverlessly and is available in SageMaker Studio and the SageMaker Python SDK, supporting several foundation models in specific regions.

🔒 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 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 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.

🔒 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.

🧠 Amazon Bedrock AgentCore Memory now supports metadata on long-term memory (LTM) records, enabling agents to tag, filter, and retrieve memories using structured attributes alongside semantic search. You can define up to ten indexed keys per memory resource with STRING, NUMBER, and STRING_LIST types and apply operator filters to refine retrieval results. Metadata can be attached at ingestion or inferred automatically by the LLM using extraction instructions defined on the memory resource. This capability is available today in all AWS Regions where AgentCore Memory is supported.

🔒 Amazon announces that Bedrock AgentCore is now available in the AWS GovCloud (US-West) Region, bringing enterprise-grade agentic AI to workloads with elevated compliance and data residency requirements. AgentCore is a managed platform for building, deploying, and operating AI agents at scale without customers managing infrastructure. Core capabilities include AgentCore Runtime for session-isolated, long-running agents; AgentCore Gateway, which uses the Model Context Protocol to convert APIs and Lambda functions into agent-ready tools with controlled access to enterprise services; AgentCore Identity for integrated authentication and permission delegation; and AgentCore Observability and Evaluations for real-time monitoring and continuous quality assessment in production.

🔒 Amazon announced AgentCore availability in the AWS GovCloud (US-West) region, bringing enterprise-grade agent capabilities to workloads with elevated compliance needs. AgentCore is a managed platform for building, deploying, and operating AI agents at scale without customers managing infrastructure. Its composable services—including Runtime, Gateway, Identity, Observability, and Evaluations—enable session isolation, long-running workloads, secure access to enterprise data via the Model Context Protocol, and integration with existing identity providers.

🚀 Amazon Bedrock AgentCore is now available in the AWS South America (São Paulo) Region. AgentCore provides a platform to build, connect, and optimize agents with runtime, identity, gateway, policy, observability, code interpreter, and browser tools available at launch. Customers can deploy agents closer to end users to reduce latency and meet data residency requirements, with security enforced at the infrastructure layer that agents cannot bypass.

🚀 Amazon Bedrock AgentCore Runtime now supports Node.js as a managed language runtime for direct code deployment, joining existing Python support. Developers can package Node.js apps or compiled TypeScript projects and their dependencies into a .zip, upload to Amazon S3, and create an agent runtime without building container images. Node.js agents gain session isolation, built-in SigV4 and OAuth 2.0 authentication, bidirectional streaming, managed session storage, and observability via Amazon CloudWatch and the AWS Distro for OpenTelemetry Node.js auto-instrumentation.

🚀 Amazon Bedrock AgentCore introduces a managed harness (preview), an AgentCore CLI, and prebuilt skills to help developers build and iterate on agents faster. The managed harness runs the complete agent loop in a per-session microVM with filesystem and shell access, supports model-agnostic switching, and lets teams export orchestration as Strands-based code. Filesystem persistence (preview) enables suspending and resuming sessions, and the CLI integrates with AWS CDK to deploy agents with governance and audibility.