InfoWorld AI·
Separating transactional databases from analytical systems was, until recently, considered good architecture. Now, as enterprises adopt AI agents that continuously read, reason over, and act on business data, data warehouse and database vendors are increasingly deciding that separation has become a liability. Just weeks after Databricks unveiled its Lakehouse Transaction and Analytical Processing (LTAP) offering based on Neon Postgres to bring operational (OLTP) and analytical (OLAP) processing closer together, EnterpriseDB (EDB) has introduced converged analytics capabilities for its managed EDB Postgres AI database service with the same intent. Both vendors are responding to the same pressure of enabling AI agents for enterprises to operate on fresh operational data without waiting for pipelines and replicas, but EDB argues its approach starts from a fundamentally different place. “Databricks is building from the lakehouse outward, trying to pull transactional capability in through L
Read full articlePRESS RELEASE. The next bull run will be shaped by DeFi but let’s be honest: not every narrative deserves to survive it. AI agents, yield, wallet super-apps, stablecoins on DEXs, cross-chain everything—every cycle brings a new set of “inevitable” futures and quietly buries a few of them. The hard part is to understand which narratives […]
Prosus launched ToqanClaw, a no-code AI platform positioned as an European alternative to AI agents like OpenClaw.
Nvidia's toolkit fosters AI innovation across industries, enhancing enterprise capabilities and solidifying its influence in the AI ecosystem. The post Nvidia Agent Toolkit enables enterprises to build domain-specific AI agents appeared first on Crypto Briefing.
Stockholm-based startup Fika Jobs is building a video-first hiring platform that combines AI interview agents with short-form video profiles, creating something that feels like a cross between LinkedIn and TikTok.
In the past year, the enterprise AI ecosystem has gained enormous capability and zero consensus. Developers now have a remarkable set of tools for building AI agents: OpenAI’s frameworks, Anthropic’s Claude tooling, LangChain, LangGraph, CrewAI, Microsoft AutoGen, and a growing list of alternatives. Each promises to coordinate reasoning loops, manage multi-step task execution, and connect agents to tools and APIs. For experimentation, the progress has been substantial. Teams can now assemble sophisticated agent workflows in days that would have taken months two years ago. But I’ve watched this pattern before. In over two decades of building and selling distributed systems platforms, I’ve seen the same dynamic play out across nearly every major infrastructure shift: the tools for consuming a new capability arrive before the infrastructure for governing it does. The gap that emerges isn’t immediately obvious in development environments. It becomes obvious in production. That’s exactly wh
Telecom operators have seen remarkable returns from using generative AI to automate network management, customer care and back-office operations. Most of that impact has been task‑based: automation that speeds up predetermined steps while people manually correlate insights and direct next steps. Automation is no longer the finish line — it’s the launchpad to autonomy. The […]
Teams are moving AI agents from prototype to workflow fast. One agent gets connected to a document store. Another starts calling internal tools. A third begins touching customer data. Soon, agents are operating across systems before governance teams have a clear record of what they can access, who owns them, or what they’ve done. AI... The post Shadow agents: find and govern unsanctioned AI agents appeared first on DataRobot.
AI agents need more than guardrails.
