Amazon SageMaker AI announces container image caching for inference, reducing end-to-end latency by up to 2x for generative AI models during scale-out events.

Factory AI leveraged LangSmith's observability and feedback API to close the product feedback loop, achieving a 2x improvement in iteration speed and significant reductions in development cycle time.

Open SWE is an open-source, cloud-hosted coding agent that autonomously handles GitHub tasks—planning, coding, testing, and opening PRs. It features a multi-agent architecture, human-in-the-loop control, and asynchronous execution.

Monte Carlo built an AI Troubleshooting Agent on LangGraph and debugged with LangSmith to help data teams resolve issues faster by exploring multiple investigation paths in parallel.

LangSmith launches public benchmarks and evaluation dataset sharing to help developers compare LLM architecture performance. The first benchmark is a Q&A dataset over LangChain docs, accompanied by the langchain-benchmarks package. The article analyzes various models and architectures, providing insights into performance and debugging.

LangSmith's homepage is now organized into Observability, Evaluation, and Prompt Engineering, with improved Resource Tags for flexible resource grouping. Onboarding guides and upcoming ABAC enhance usability.

Agent engineering is an emerging discipline that integrates product thinking, engineering, and data science to build reliable LLM agents through rapid iteration and production feedback. It addresses the unpredictability of agents by cycling through build, test, ship, observe, and refine, as practiced by companies like Clay, Vanta, LinkedIn, and Cloudflare.

Evaluate and compare fine-tuned open source LLMs using LangSmith. Test multiple models, automate evaluations, and choose the best performing AI.

AI agents work best when they reflect the knowledge and judgment your team has built over time. This article explores how to integrate human judgment into each stage of agent development, using a trader copilot example. It covers workflow design, tool design, and context engineering, and emphasizes the importance of automated evaluations and continuous iteration.

Learn how Deep Agents SDK manages context for long-running AI tasks through offloading, summarization, and filesystem abstraction to prevent context rot.

Databricks announced three new capabilities at Data + AI Summit 2026: App Spaces for governance, Genie App Builder for AI-assisted app creation, and Serverless Micro Apps for cost-efficient scale-to-zero apps, enabling governed vibe coding in the enterprise.

This post walks you through how to use P-EAGLE directly within Amazon SageMaker AI. It demonstrates how to select a compatible model from SageMaker JumpStart, configure parallel drafting, and deploy a highly optimized endpoint.

Databricks announced OpenSharing, the next evolution of Delta Sharing and the industry's first open protocol built for the agentic era. It extends data sharing to the full AI stack — models and agents — and becomes an independent open-source project under the Linux Foundation. OpenSharing enables sharing across any cloud, vendor, and format, addressing challenges of cross-organizational data collaboration. Key features include Genie Agent Sharing for governed AI experiences, SecureConnect for simplified cross-cloud networking, Global Distribution for automatic replication and reduced egress costs, and support for on-premises storage via the Storage Ecosystem. It also adds Apache Iceberg REST Catalog API compatibility for broader interoperability.

Databricks announces the Public Preview of Apps on Databricks Marketplace, enabling customers to discover, install, and run third-party data and AI applications natively within their secure Databricks workspaces, with no data movement required.