Agentic AI

Amazon Redshift now pairs with the AWS Model Context Protocol server to allow AI coding assistants like Claude Code and Cursor to manage data warehouses directly. This integration leverages curated Redshift skills that provide authenticated API execution, SQL syntax references, and metadata discovery capabilities. The goal is to reduce manual query writing and streamline tasks such as schema exploration, data loading, and materialized view optimization.

• AI agents can now execute authenticated AWS API calls for Redshift tasks via the MCP server.
• Curated skills reduce SQL generation errors with syntax references and best practices.
• Metadata discovery allows schema exploration without manual SQL writing.
• Supports common patterns for data loading, pivoting, and materialized views.

Research on five open-weight models reveals that tool-using agents fail by selecting wrong tools or generating incorrect arguments, with errors propagating silently downstream. Under free-running conditions, approximately 70% of a model's clean-context capability is lost by step six due to compounding earlier mistakes. The study also highlights that exact-match scoring metrics inherently fix the estimated severity and recovery parameters of propagation models.

• Agent errors compound quickly, losing ~70% of capability by step 6 in free-running modes.
• Distinguish between wrong tool selection and wrong argument generation when diagnosing failures.
• Exact-match scoring against gold trajectories biases severity and recovery parameter estimates.
• Clean teacher-forced context yields significantly higher reliability than free-running agent loops.
• Multi-step tasks (depth 1-8) show clear degradation trends across five open-weight models.

Despite standard protocols supporting pagination for large tool responses, analysis of real-world session logs reveals that coding agents never request subsequent chunks. Researchers treat first-chunk selection as a 0/1 knapsack problem to maximize the precision-at-1 rate, ensuring the required item appears in the initial token window. Benchmarked against 500 SWE-bench Verified tasks, this approach outperforms existing methods by optimizing for what the agent actually reads rather than what is technically available.

• Agents consistently skip pagination, making first-chunk content the sole determinant of success
• Model Context Protocol logs confirm zero agent-initiated requests for second chunks
• First-chunk selection modeled as a 0/1 knapsack problem improves precision-at-1 rates
• Optimizing for the first chunk is more critical than supporting full response retrieval
• Benchmarked on 500 SWE-bench Verified tasks to validate value function effectiveness

NVIDIA has released the Cosmos3-Edge, Cosmos3-Nano, and Cosmos3-Super models on Amazon SageMaker JumpStart, expanding access to open frontier omni-modal world models. These models enable developers to build robots, autonomous vehicles, and vision AI systems capable of perceiving, reasoning, and acting in physical environments. The Cosmos3-Edge variant is specifically optimized for on-device control, featuring a 4B-parameter architecture with a 2B Nemotron-based reasoner for real-time visual reasoning.

• Three new Cosmos3 models (Edge, Nano, Super) are now accessible via SageMaker JumpStart for AWS customers.
• Cosmos3-Edge targets edge hardware with 15 Hz inference, generating 32 actions per cycle at 640x360 resolution.
• The Edge model uses a 4B-parameter omni-model paired with a 2B Nemotron reasoner for robot control.
• These open models support building physical AI systems for robotics and autonomous vehicle applications.

AWS has extended Amazon Bedrock AgentCore availability to the US West (N. California) and Asia Pacific (Hyderabad) regions. This platform enables engineers to build, connect, and optimize AI agents using any framework or model while enforcing infrastructure-layer security. The expansion allows customers to deploy agents closer to end users for reduced latency. All core capabilities, including runtime, identity management, and observability, are available at launch in these new locations.

• Bedrock AgentCore is now live in US West (N. California) and Asia Pacific (Hyderabad).
• Deploy agents closer to users in these regions to reduce network latency.
• Security policies are enforced at the infrastructure layer, bypassing agent constraints.
• Full feature parity at launch: runtime, IAM, policy management, and observability.
• Supports any AI framework and model for building enterprise-grade agents.

Agent Seer generates realistic evaluation scenarios for AI agents by parsing Model Context Protocol (MCP) specifications. It extracts semantic information from function names, descriptions, and parameter schemas to create tests without manual curation or live tool execution. This approach addresses the scalability and static nature issues of traditional benchmark construction.

• Eliminates manual curation by deriving scenarios directly from MCP specs
• No live tool access or example data required for scenario generation
• Captures tool composition and iteration across conversation turns
• Scales across tool ecosystems without deep domain expertise
• Avoids static benchmarks that fail to track evolving APIs