The enterprise conversation about agentic AI is obsessed with the agent's logic. This session argues that the agent is the easy part—the sandbox it runs in is what actually determines whether the deployment survives contact with reality. Drawing on the Probe/Sandbox/Executor architecture, we will discuss why teams that invest in the environment ship reliable agents, while those focusing solely on the agent ship demos. We'll explore why reliability, safety, and cost control are properties of the environment, not the model. Key Takeaways: Environment First: Why the sandbox environment matters more than the agent's logic for security. Probe/Sandbox/Executor: Implementing this default framework for enterprise agents. Constraint Design: Using environment design to naturally prevent failure modes. Reproducibility: Ensuring reliability across deployments by standardizing the sandbox.
Vsevolod is a Founding Engineer architecting advanced autonomous agentic systems. He was previously a Data Engineer at Thrad.ai, developing end-to-end algorithms for conversational ad networks. His work focuses on the intersection of quantitative modeling and LLM infrastructure, specifically building secure execution environments and advanced memory architectures for production-grade AI.
