Systems | Development | Analytics | API | Testing

Struggling to scale your AI? You aren’t alone. Shafrine from WSO2 identifies the bottleneck holding companies back: Data Silos. Without integration, your AI agents lack the "context" needed to be useful in a production environment. Learn how to bridge the gap between a "cool pilot" and a "scalable enterprise agent" by fixing your fragmented workflows.

Generative AI offers incredible potential, but it comes with real risks like data leakage and prompt attacks. In this video, we demonstrate how WSO2 AI Guardrails act as an intelligent filter to secure your AI integrations and ensure compliance. We walk through the configuration of four critical advanced guardrails to inspect both incoming requests and outgoing responses, helping you move from risky experiments to safe, reliable production services.

In this episode of, *Steve Jordan* and *Shafreen Anfar* from WSO2 explore how integration is paving the way for the agentic enterprise, where humans and AI agents collaborate to drive business success. They discuss how seamless connectivity across systems provides agents with the real-time context and ability to take action that is necessary to scale AI from simple pilots to full-scale production. The conversation also highlights the importance of robust security, governance, and observability in managing this new digital workforce.

Generative AI offers incredible potential, but it comes with real risks like data leakage and prompt attacks. In this video, we demonstrate how WSO2 AI Guardrails act as an intelligent filter to secure your AI integrations and ensure compliance. We walk through the configuration of four critical advanced guardrails to inspect both incoming requests and outgoing responses, helping you move from risky experiments to safe, reliable production services.

Learn how to ensure consistent AI interactions and drastically reduce latency using the WSO2 AI Gateway. This step-by-step tutorial demonstrates how to standardize your LLM requests for quality and efficiency while cutting down on redundant API costs. We explore "Prompt Management" to enforce organizational guidelines using templates and decorators, and "Semantic Caching" to leverage vector embeddings—serving instant, cached responses for semantically similar queries to minimize expensive LLM calls.