As agentic AI systems evolve from simple assistants to powerful autonomous agents, they introduce a fundamentally new set of architectural risks that traditional AI security approaches don’t address. Agentic AI can autonomously plan and execute multi-step tasks, directly interact with systems and networks, and integrate third-party extensions, amplifying the attack surface and exposing serious vulnerabilities if left unchecked.

In this webinar, we’ll break down the most common security failures in agentic architectures, drawing on real-world research and examples from systems like OpenClaw. We’ll then walk through secure design patterns for agentic AI, showing how to constrain autonomy, reduce blast radius, and apply security controls before agents are deployed into production environments.

This session establishes the architectural principles for safely deploying agentic AI. Part 2 builds on this foundation by showing how these weaknesses are actively exploited, and how to defend against real agentic attacks in practice.

Key Takeaways

Attendees will learn how to:

• Identify the core architectural weaknesses unique to agentic AI systems
  
  
• Understand why traditional LLM security controls fall short for autonomous agents
  
  
• Apply secure design patterns to limit agent permissions, scope, and authority
  
  
• Architect agents with guardrails around tool use, memory, and execution
  
  
• Reduce risk from prompt injection, over-privileged agents, and unintended actions