A recent IBM–Morning Consult survey shows that 99% of developers building AI applications are already exploring or building AI agents. Yet, despite this momentum, most organizations still hesitate to deploy agents into production. The core barrier is a trust deficit—driven by concerns around robustness, reliability, safety, and policy alignment. In this keynote, we’ll unpack the hidden pitfalls that prevent AI agents from crossing the chasm from development to real-world use. From adversarial robustness testing—including susceptibility to prompt injection and other attacks—to “natural robustness” failures where small input variations trigger wildly inconsistent outputs, we will explore why traditional evaluation often breaks down. We will also examine how continuous guardrails and runtime policy checks can serve as the backbone for trusted deployment. Attendees will walk away with practical best practices, architectural patterns, and evaluation strategies that enable AI agents to earn the trust of risk, compliance, and engineering teams—ultimately accelerating their path to production.

What You Will Learn

• How to design, evaluate, and harden AI agents for robustness against adversarial attacks.
• How to ensure evaluation stability so that an agent’s behavior remains consistent from development to production.
• How to implement continuous guardrails and governance controls that keep agent behavior aligned with organizational policies.
• How to build a reliable production pipeline for agents that can withstand real world variability and edge cases.

Who Should Attend

• AI application developers
• AI agent builders
• AgentOps teams
• Technical leads
• Engineering managers
• DevOps engineers
• and risk or compliance professionals who want to confidently deploy AI agents into production and unlock their full business value.