Systems | Development | Analytics | API | Testing

Large Language Models (LLMs) like ChatGPT and Claude offer powerful ways to extract insights from enterprise data. But connecting them directly to your backend databases—without security safeguards—can lead to disaster. A naïve setup, such as giving an LLM raw SQL login credentials, exposes your business to massive risk: credential leaks, SQL injection attacks, and unauthorized data access.

Multi-agent workflows are the latest technological gen AI advancements. In this blog, we explore how to develop such systems, overcome operational challenges, improve system observability, and enable seamless collaboration between agents in complex AI pipelines. We’ll cover architecture, A2A and MCP protocols and introduce Google Cloud’s agentic marketplace.

As Artificial Intelligence (AI) becomes deeply embedded in decision-making across fraud detection, chatbots, and virtual assistants, trust in AI agents is now critical. Users and stakeholders need clear assurance that these systems will behave fairly, clearly, evidently, and reliably in all situations. However, building that trust does not happen by chance; it requires smarter testing strategies specifically designed for the non-deterministic and robust nature of AI.

Discover insights from a recent TDWI expert panel on integrating data and AI platforms.

We are entering a new era in enterprise data: the era of the Data Operator. As AI becomes core to every business process, every team is being asked to move faster, act smarter, and operate with real-time data. But the old stack isn't built for that. It's built for centralization. For gatekeeping. For data engineers and IT teams to own every flow, sync, and transformation. That model is breaking down. Why? Because the need for data has exploded at the edge of the business. Customer teams. RevOps.