InfoWorld AI·
As enterprises rush to build AI agents that can reason over business data and take action, Databricks argues that the long-standing practice of separating operational and analytical data systems is turning into a liability. That separation, the cloud-based data warehouse provider says, is becoming increasingly strained as AI agents require simultaneous access to live operational data and historical context to make decisions and take actions in real time, unlike humans, who traditionally can work with data that is minutes or hours old. At its annual Data + AI Summit, the data warehouse provider introduced Lake Transactional and Analytical Processing (LTAP), a new architecture designed to unify transactional and analytical data on a single storage layer. The new approach, according to Databricks, differs from traditional online transaction processing (OLTP) and online analytical processing (OLAP) architectures, which typically store operational and analytical data in separate systems. Tr
Read full articleCoinbase's AI-driven platform could revolutionize crypto adoption, streamlining autonomous transactions and reshaping financial infrastructure. The post Coinbase confirms AI agents will spend x402 credits on research data and tooling appeared first on Crypto Briefing.
Developers are caught between the joy — or pressure — of using agents to ship 10x faster today and the dread of how they will maintain that code tomorrow. The gap between “vibe” code and code that can be deployed to millions of users is vast and easy to underestimate. Closing the gap requires care, expertise, and effort, with the payoff coming later. Agents are able to complete increasingly complex programming tasks but without the quality we need. What’s missing, and how can we fill the gap? Sonar Why agent-generated code degrades: the bloat problem Enterprise code has to clear three bars: it must be maintainable, reliable, and secure. Out-of-the-box AI agents can miss all three. Let’s focus on the biggest and most visible maintainability issue, which is bloat: redundant validation, defensive checks that cannot fire, near-duplicate functions, dead code that nothing removes. A None check on a parameter typed as dict. A try/except around a call that never throws. Two functions, ide
The concept of vibe coding is interesting; you don’t need to be a developer or software engineer to build your own applications. You can describe your idea to an AI in plain language, and it will build, edit, and refine your applications so you don’t have to write code line by line. It sounds simple […] The post Meet Atoms: A Vibe Coding Tool That Uses AI Agents to Build, Deploy, and Market Your App (No Code) appeared first on MarkTechPost.
Respond.io, one of Malaysia startups to watch, uses AI agents to handle high volumes of customer inquiries and charges per convo, not per seat.
This common comparison invites us to see ourselves as sub-optimal alternatives to AI agents
Arcade.dev raised $60M in Series A funding to build the authorization and governance layer enterprise AI agents need to reach production safely.
Nvidia's strategic expansion into AI CPUs could redefine AI infrastructure, potentially capturing significant market share and boosting revenue. The post Nvidia’s Jensen Huang unveils Vera CPU designed for AI agents appeared first on Crypto Briefing.
Nvidia's Vera CPU could redefine AI infrastructure, challenging traditional CPU markets and potentially reshaping tech industry dynamics. The post Nvidia unveils Vera CPU designed for AI agents, not humans, says Jensen Huang appeared first on Crypto Briefing.
