AI systems are rapidly evolving from passive models into autonomous agents capable of executing complex workflows across APIs, services, and cloud infrastructure. While this unlocks unprecedented automation, it also introduces a new and largely unaddressed category of risk.

In this talk, we explore agentic infrastructure risk - the failure modes, security vulnerabilities, and operational hazards that emerge when AI systems are allowed to take real-world actions. Drawing from experience building large-scale infrastructure at Meta, we examine how agent runtimes interact with APIs, tools, and micro-services, and why traditional security and reliability models fail in non-deterministic systems.

We will analyze critical emerging risks, including hallucinated actions, cascading retries, privilege escalation through tool misuse, and unbounded execution loops that can trigger system-wide incidents or cost explosions. Unlike traditional software failures, these risks are amplified by autonomy and feedback loops.

The session will introduce practical mitigation strategies:

* Guardrail architectures and action validation pipelines
* Policy-driven execution controls and zero-trust action models
* Observability for reasoning-driven, non-deterministic systems
* Cost containment and runtime safety mechanisms

Attendees will leave with a concrete, production-ready blueprint for designing AI systems that are secure, observable, and governed by design - enabling organizations to deploy autonomous agents without compromising safety or control.


Key Takeaways
- Identify new classes of AI risk introduced by autonomous agents
- Understand how agent failures differ from traditional system failures
- Learn to design guardrails and zero-trust execution layers
- Build observability for decision-making systems
Apply cost, safety, and governance controls in production