Taming Rogue Agents: Real-Time Mitigation Strategies for Multi-Agent and Multimodal AI at Scale
About This Session
As Large Language Models (LLMs) become the foundation for multi-agent and multimodal systems, spanning voice assistants, chat interfaces, generative media tools, and autonomous agents, the risk of rogue or misaligned behavior is accelerating. Failures now make headlines, from chatbots producing unsafe or biased responses, to voice systems offering incorrect or harmful advice, to autonomous agents executing unintended actions.
This talk will present an industry level framework for detecting, containing, and preventing rogue AI behavior in real time. Drawing on cross-modal examples, spanning conversational AI, multimodal reasoning agents, and enterprise AI deployments. We will explore:
• Common failure modes in multi-agent architectures (e.g., policy overreach, unsafe delegation, compounding errors).
• Real-time mitigation playbooks, including autonomous hotfix pipelines, selective guardrails, and trust signal monitoring.
• Scalable safety strategies that balance rapid containment with minimal disruption to end users.
Attendees will leave with actionable guidance for operationalizing rogue AI mitigation, grounded in lessons from large scale, high traffic AI deployments across the industry.
Learning Objectives:
1. Identify systemic risks and rogue behaviors in multi-agent and multimodal AI.
2. Apply scalable, real-time guardrail and hotfix strategies across different AI modalities.
3. Design governance and escalation processes that prevent isolated failures from becoming systemic incidents.
This talk will present an industry level framework for detecting, containing, and preventing rogue AI behavior in real time. Drawing on cross-modal examples, spanning conversational AI, multimodal reasoning agents, and enterprise AI deployments. We will explore:
• Common failure modes in multi-agent architectures (e.g., policy overreach, unsafe delegation, compounding errors).
• Real-time mitigation playbooks, including autonomous hotfix pipelines, selective guardrails, and trust signal monitoring.
• Scalable safety strategies that balance rapid containment with minimal disruption to end users.
Attendees will leave with actionable guidance for operationalizing rogue AI mitigation, grounded in lessons from large scale, high traffic AI deployments across the industry.
Learning Objectives:
1. Identify systemic risks and rogue behaviors in multi-agent and multimodal AI.
2. Apply scalable, real-time guardrail and hotfix strategies across different AI modalities.
3. Design governance and escalation processes that prevent isolated failures from becoming systemic incidents.
Speaker
Sundar Chandrasekaran
Principal Product Manager - Alexa AI Trust & Safety - Amazon
Sundar Chandrasekaran is a senior leader at Alexa AI, where he heads Trust & Safety initiatives for generative and multi-agent AI systems. His work spans global-scale Responsible AI programs, with a focus on building trustworthy, efficient, and safe AI applications. Sundar has developed real-time mitigation frameworks and governance models that protect millions of AI interactions daily across domains, ensuring policy compliance and user trust at scale.