AI can predict tokens but doesn’t understand safety. With the explosion of MCP and autonomous agents, business expect them to seamlessly call APIs, execute tasks, and/or read sensitive data. The primary blocker in getting agents from experimentation and into production is the ability to do all the above safely.
Join Sina Jahan, Head of Product Engineering for a webinar where he'll discuss at how to architect data infrastructure that treats safety as a core feature instead of an afterthought.
Who You'll Learn
During the event, he'll cover:
- How to safely expose multi-modal, domain-scoped data to GenAI agents
- Why traditional ACLs and role-based access fall short in real-time, AI-driven systems
- How to build AI-native guardrails: per-action permissions, hard policy enforcement, and human checkpoints
- Implementation examples from Nextdata OS, including MCP-based control and safety context that travels with data
There will also be time for Q&A.
Who Should Attend
- Data engineers scaling GenAI applications beyond prototypes
- ML and AI engineers implementing autonomous agent workflows
- Infrastructure engineers designing AI-native data platforms
- Technical leaders evaluating data architecture for GenAI production systems
Whether you're building agents, internal copilots, or AI automation into business workflows, this session will help you do it without risking the integrity of your systems or data.
