Enterprise software news — SaaS platforms, ERP, CRM, productivity suites, and AI integrations in business software.
"7-dimension resilience framework" for building trust in AI agent systems, arguing that the most dangerous failures are not crashes but "drifts" where agents appear to function correctly while making subtle, undetected e…
MCP (Model Context Protocol) server integrations are not inherently dangerous, but the primary security risk lies in how vendors build and configure them. It warns that most buyers fail to scrutinize critical architectur…
The primary risk from AI in software development is not poor code quality but a lack of code provenance, traceability, and attribution. It advises teams to shift focus from choosing the best AI model to improving workflo…
AI-generated SQL queries often appear correct but contain subtle errors due to insufficient prompting, not AI limitations. It emphasizes that providing proper schema context, business term definitions, and few-shot examp…
According to the article, the most effective way to improve AI coding agents' performance is not through better prompts or models, but by maintaining a "boring" and highly consistent codebase. The author argues that agen…
AI agent failures in production are often caused not by model quality but by underlying networking issues such as latency, packet loss, and protocol behavior, which are frequently overlooked. It highlights that common ag…
Google Beam has introduced a new experiment that uses HP Dimension's immersive display to render remote participants in true-to-life size and position them as if seated around a table, paired with spatial audio to anchor…
The Model Context Protocol (MCP) is an open standard published by Anthropic that enables AI assistants to securely discover, call, and receive results from structured tools, bridging the gap between AI and CRM systems. T…
Urban Outfitters (URBN) migrated its IBM Sterling Order Management System from an Oracle database to Google Cloud's AlloyDB for PostgreSQL to reduce costs and improve scalability. The 11TB database migration was complete…
General availability of GKE Agent Sandbox, an open-source, Kubernetes-based execution environment designed for secure and scalable AI agent workloads, featuring capabilities like pod snapshots for idle compute reduction …
Google has introduced Agent Executor, an open-source runtime standard designed to reliably manage long-running, distributed agent workflows in production. The system addresses key operational challenges by providing dura…
A system called "Minions," inspired by a Stripe blog post, which uses a markdown-based orchestrator to run multiple AI coding agents in parallel on independent tasks. Each agent operates in an isolated git worktree to av…
Markus is an open-source AI Workforce Platform that functions as a complete operating system for digital employees, featuring a team of specialized agents (managers, developers, reviewers) that collaborate under unified …
A feature store is the operational center of a real-time ML system, responsible for storage, serving, registry, and consistency enforcement. Its central design challenge is achieving both consistency and low latency at s…
The Metabase 61 update introduces granular AI governance controls, allowing administrators to manage AI feature access per user group and set token limits for internal and embedded analytics. New features include an MCP …
Key principles for building trustworthy AI automation, emphasizing the importance of transparent logging, user autonomy, and privacy-by-design. It uses examples like a smart home agent with action logging, a Shutterstock…
Grab's Analytics Data Warehouse team implemented a multi-agent AI system to automate engineering support workflows, handling tasks like SQL debugging and platform troubleshooting to reduce repetitive operational work. Th…
Per-user OAuth is essential for AI agents because it allows each individual user to grant the agent a scoped, revocable token tied to their own account, solving the security and auditability problems caused by shared API…
Multi-agent AI orchestration fails without proper configuration, comparing it to Olympic runners in a relay race who don't know the order or handoff protocol. It details how different tools like Claude Code, OpenCode, an…
Aspect-oriented programming (AOP) principles for AI agents by using "hookflows" as an event bus to handle cross-cutting concerns like notifications. Instead of having each agent explicitly call a Telegram notification fu…