Deep research agents that combine private documents with web search can inadvertently leak sensitive information through their query logs. The MosaicLeaks benchmark quantifies this privacy risk and proposes a training method called Privacy-Aware Deep Research (PA-DR) that reduces information leakage by over 3x while maintaining task performance.

We break down the technical architecture behind our multi-stage vulnerability discovery harness and automated triage loop. Learn how we manage state controls, squash false positives through adversarial review, and route around LLM context limits.

LangSmith launches a no-code agent builder that enables non-technical users to create AI agents with memory, guided prompts, and MCP tools. The builder uses conversational guidance, built-in memory, and sub-agents to lower the barrier for agent development, suitable for internal productivity use cases.

Amazon Bedrock AgentCore harness is now generally available, allowing developers to create and run a fully functional agent with just two API calls. It provides an isolated runtime environment, built-in memory, tool integration, skill libraries, and real-time tracing without writing orchestration code or building containers.

Stagwell developed a privacy-safe identity matching solution using Databricks Clean Rooms and Marketplace Apps. Brands can install the app in their own environment, run identity matching against Stagwell's Identity Spine without exposing raw data, and activate audiences through its Agentic Targeting System. The approach reduces deployment from months to minutes and ensures compliance.