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The talk explores how Rust’s type system and memory safety can be leveraged to enforce mandatory guardrails at the infrastructure level, where traditional frameworks often fall short.
As autonomous AI agents move from prototypes to production, the gap between Python’s flexibility and the need for rigorous safety becomes a critical liability. This session introduces the "Iron Cage" architecture - a hybrid approach that utilizes Rust as a secure, high-performance runtime boundary for AI agents.
The talk explores how Rust’s type system and memory safety can be leveraged to enforce mandatory guardrails at the infrastructure level, where traditional frameworks often fall short. Through a real-world case study, the speaker demonstrates design patterns for wrapping unpredictable AI logic in a secure Rust environment. Attendees will learn how to transition from optional application-level validators to a system where safety and resource constraints are enforced by the runtime itself. The session provides a blueprint for building AI-native infrastructure that ensures production-grade reliability without sacrificing development velocity.
In my session, I will present the https://hotpath.rs crate and explain how it compares to other profiling tools available.
This talk puts popular Rust rewrites to the test. We'll examine how these tools stack up against their battle-tested predecessors, looking at real-world performance, compilation times, binary sizes, feature completeness, and ecosystem maturity.
In this talk, we’ll explore battle-tested best practices for integrating Claude Code into a professional Axum development workflow without compromising on Rust’s core values: correctness, clarity, and maintainability.
