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A recent study by PwC suggests that AI could contribute up to $15.7 trillion to the global economy by 2030, with automation playing a key role in boosting efficiency and innovation. AI agents are central to this transformation, streamlining workflows, handling repetitive tasks, and enabling data-driven decision-making. From virtual assistants in customer service to intelligent fraud detection in finance, these agents are reshaping industries and driving business growth.

Large language models are under threat from a tactic called LLM grooming, where bad actors flood public data sources with biased or misleading content to influence AI training behind the scenes. Hemraj Bedassee , Senior Solutions Manager, AI Testing,

Generative AI is quickly becoming ubiquitous in the software development space, with tools like Anthropic’s Claude offering rapid methodologies for code iteration, testing, and deployment. As new solutions, such as MCP (Model Context Protocol), are created to make integration more seamless, enterprises are adopting these AI solutions to optimize their development processes, a familiar challenge repeatedly arises: cost.

Before we delve into agentic RAG and AI agents, let’s take a moment to acknowledge that the world of artificial intelligence is evolving at a tremendous pace. From the initial excitement surrounding large language models (LLMs) to the practical application of generative AI (Gen AI), businesses are constantly finding new ways to automate tasks and innovate faster.

The rise of natural language query (NLQ) technology in modern business intelligence (BI) and analytics platforms is empowering many companies to streamline data exploration and analysis, and democratize access to insights for more people - not just data experts. But like any technology, the ongoing challenge is to help stakeholders and customers to see the value in using it.