Just as some problems are too big for one person to solve, some tasks are too complex for a single artificial intelligence (AI) agent to handle. Instead, the best approach is to decompose problems into smaller, specialized units so that multiple agents can work together as a team. This is the foundation of a multi-agent system—networks of agents, each with a specific role, collaborating to solve larger problems. When building a multi-agent system, you need a way to coordinate how agents interact.

BigQuery ML adds TimesFM, LLM-based structured output generation, row-wise inference functions, expanded model choice, and Contribution Analysis GA.

Emerging technologies like generative AI (GenAI) and agentic AI are poised to significantly enhance IT operations. These advancements offer new, truly transformative ways to manage, optimize and automate IT environments, and are certain to improve efficiency and foster innovation. GenAI’s ability to process vast amounts of unstructured data and agentic AI’s autonomous decision-making capabilities span predictive analytics to automated problem-solving.

Just like every impressive building starts with a strong foundation, every remarkable capability in an AI agent can be traced back to its framework. AI agent frameworks or agentic AI frameworks make it possible to create smart, efficient AI agents that can serve as simple chatbots, facilitate agentic automation, or contribute to complex use cases in finance, supply chain, healthcare, manufacturing, and robotics as part of a multi-agent system. But what are AI agent frameworks?

Have you ever felt personally offended by a 4-star review, even after giving 110% to your customers? You’re not alone. Even the smallest product hiccup can spark a Reddit thread or a sarcastic TikTok. Getting customer engagement right is more challenging and important than ever. You will likely face ongoing challenges in customer engagement with new and evolving expectations, and meeting these needs is vital to fostering brand loyalty.

The architecture of the modern data lake offers considerable advantages over data warehouses.

Are you leading the development or marketing of an app or website that involves protected health information (PHI)? If you have not heard the term before, PHI involves information related to an individual’s health status, healthcare, and healthcare payments. The Health Insurance Portability and Accountability Act (HIPAA) protects said individuals and their health-related information, which you must comply with before progressing further with your product or service.

The retail industry has been shaped and fundamentally transformed by disruptive technologies in the past decade. From AI assisted customer service experiences to advanced robotics in operations, retailers are pursuing new technologies to address margin strains and rising customer expectations.

BigQuery DataFrames 2.0 brings multimodal data processing and AI into your BigQuery Python workflows, and enables larger Pythonic data science.