Gayatri
Tavva
Senior Data Engineer at Amazon
"Governed Multi-Agent Architectures for Enterprise Analytics and Decision Systems"
"Governed Multi-Agent Architectures for Enterprise Analytics and Decision Systems"
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Session: Governed Multi-Agent Architectures for Enterprise Analytics and Decision Systems
As AI agents move from experimentation to production, enterprises face a critical challenge: how to scale multi-agent systems without sacrificing trust, accountability, and governance. This talk presents a practical, architecture-first approach to building governed multi-agent AI systems designed for real-world enterprise analytics and decision-making.Drawing from hands-on experience designing AI-enabled platforms for high-stakes domains such as compensation and audit analytics, this session demonstrates how role-based agents—spanning data retrieval, validation, reasoning, compliance, and execution—can collaborate within clearly defined boundaries. Attendees will see how governance is embedded at every layer through policy enforcement, explainability, auditability, and human-in-the-loop controls, rather than added as an afterthought.
Through a live architectural walkthrough and hands-on examples, the talk contrasts ungoverned versus governed agent workflows, highlighting why responsibility, traceability, and trust are essential for enterprise adoption. Participants will leave with concrete design patterns and practical guidance for building multi-agent systems that leaders can confidently deploy in production.
Key Takeaways
- How to design governed multi-agent architectures where each agent has clear responsibility and boundaries.
- When and why to apply human-in-the-loop controls in high-stakes AI decision systems
Bio
Gayatri Tavva is a distinguished data and AI engineering leader who designs enterprise-scale, governed AI systems for analytics and decision support. Her work bridges deep technical architecture with AI, governance, and real-world business impact.