Generative AI has evolved at lightning speed from LLMs and RAG to autonomous AI agents capable of reasoning, planning, and acting. But creating a single agent is easy; managing thousands in an enterprise requires a full AI Agent Platform. This guide breaks down the architecture of a production-grade platform, covering layers like Interaction, Development, Core, Foundation, Information, Observability, and Trust. It shows how to build systems that are secure, scalable, and capable of delivering real business impact.

Agents sound smart until millions of users show up. A real talk on tools, UX, and why autonomy is overrated.

Learn how a natively multimodal database like ApertureDB helps healthcare ads stay compliant by flagging missing facts and improving transparency by supporting true multimodality alongside vector search. Longer abstract: Technologies like RAG (retrieval-augmented generation), semantic search systems, and generative applications wouldn’t be possible without vector databases. A very few of these databases, such as ApertureDB, are truly capable of natively handling more than just text. They now work with images, audio, and other data types, which opens up new possibilities across industries like healthcare, retail, and finance. For building this example, we pick healthcare advertising because it shows a great blend of multimodality. With strict rules around accuracy, disclosure, and patient privacy, it’s critical to include all Material Facts in marketing content. These are details that could influence a patient’s understanding or choices. In this blog, we will discuss how a combination of ApertureDB, Unstructured, and OpenAI can help detect and flag missing material facts in healthcare advertisements.