Aug 18, 2026

AI Agents Gone Rogue? Build, Defend & Attack AI-Enabled Apps • Katie Paxton-Fear • YOW! 2025

Katie Paxton-Fear explores the emerging security landscape of AI agents, moving beyond chatbots to autonomous, specialized entities. She details five critical vulnerabilities: social engineering, hacking frameworks, malicious agents, AI as accidental insiders (vibe coding), and the rise of hackbots. The talk concludes with a powerful call to action for security professionals to proactively engage and influence AI agent development, emphasizing that 'abstinence-only education doesn't work' when it comes to AI adoption.

Aug 16, 2026

5 Ways to Connect AI Agents to Tools: From APIs to MCP

Grant Miller outlines five evolving patterns for integrating AI agents with tools, starting from simple direct API connections to complex, secure token-based architectures. The discussion highlights the progression of these methods, emphasizing how authentication, user delegation, abstraction layers like MCP, and secure credential management using vaults improve security, observability, and scalability in agentic systems.

Aug 10, 2026

Codex, Behind the Harness — Dominik Kundel, OpenAI

Once GPT 5.3 Codex Spark achieved 1000 tokens/sec on Cerebras, network latency superseded inference as the bottleneck for agents. This talk details how the Codex harness addresses this and other agentic challenges through innovations like WebSocket mode for stateful context, deferred tools for efficient context construction, robust sandboxing (Seatbelt, Bubblewrap, custom Windows solution), and an auto-review subagent to mitigate approval fatigue while ensuring security. It also covers structured actions via 'apply patch' for file edits, shell tools for system interaction, and sophisticated long-horizon goal management, with most distinct features exposed through the open Responses API.

Aug 10, 2026

5 Best Practices for Building AI Agent Skills

This video outlines five essential best practices for developing reliable, secure, and effective AI agent skills. It covers optimizing skill triggering through descriptive metadata, leveraging real-world domain expertise over generic LLM output, managing context windows efficiently by writing lean skills and using progressive disclosure, implementing deterministic logic with scripts for fragile operations, and critically vetting all skills for security vulnerabilities before deployment. These practices are crucial for professionals building robust agentic systems.

Jul 20, 2026

In the Land of AI Agents, the Verifiers Are King — Tariq Shaukat, Sonar

This talk addresses the critical challenge of verification in AI agent development, moving beyond generation to ensure correctness. It highlights the problem of "AI slop" and the "productivity paradox" of AI coding agents, where initial velocity gains are offset by increased technical debt and quality issues. The speaker introduces the AC/DC (Agent-Centric Development Cycle) framework comprising three stages: Guide (providing context and constraints), Verify (zero-trust, multi-layered verification using both algorithmic and agentic methods), and Solve (active code maintenance to control technical debt). This systems-level approach, integrating verification across agentic, CI, and code maintenance loops, significantly reduces issues and transforms AI into a reliable enterprise asset.

Jul 13, 2026

From fork() to Fleet: Designing an Agent Sandbox Cloud — Abhishek Bhardwaj, OpenAI

This talk by Abhishek Bhardwaj from OpenAI details the architectural considerations for building secure and scalable AI agent sandboxes in the cloud. It explores runtime isolation technologies (from basic process execution to containers, GVisor, and microVMs), emphasizing the superior security of hardware-virtualized microVMs. The speaker then highlights the critical need for persistent storage, outlining explicit (copy-on-write snapshots) and always-on (tiered block storage) solutions as the next major unlock for agent capabilities. Finally, it touches on orchestration challenges for fleet-level management, including low-latency sandbox creation and snapshot-driven scheduling.