In a standard editor, latency is binary: you type, it appears (10ms). In an Autonomous Runtime, latency is cumulative. A single "Scout Agent" might perform 5 vector lookups, 2 AST parses, and 1 inference call. If our runtime adds even 50ms of overhead per step, a 10-step reasoning chain feels sluggish. Our telemetry revealed a hard truth: While we can't speed up the LLM (yet), our Python orchestration layer was adding 30% overhead due to GC pauses and object serialization. We are declaring war on that 30%. We are currently migrating the core D3 State Engine from Python to Rust. The goal isn't just "speed"; it's Zero-Copy Memory Safety. In our Python prototype, passing a "Context Object" to a worker thread required pickling (copying) the data. In our new Rust architecture, we utilize Affine Types. The memory isn't copied; ownership is simply transferred. We identified that VectorIndex.search() was our heavy lifter. We are rewriting the hot path to bypass the Python interpreter entirely. We chose Rust not because it's trendy, but because it is the only language that allows us to build "Fearless Concurrency." We are attempting to run 10,000+ Scout Agents in parallel. In Rust, the compiler forces us to handle the state synchronization before we can even run the build. We haven't reached "Zero Latency" yet—inference is still the bottleneck—but we are successfully collapsing the infrastructure overhead. Vector Search: 40% faster in our Rust benchmarks. Memory Footprint: Reduced by 60% (No GC overhead). Stability: Zero "Stop-the-World" pauses in the new kernel. The migration to a Rust-based runtime is a foundational step in making autonomous engineering a real-time experience. By eliminating the infrastructure tax, we allow the full compute budget to flow where it matters most: the reasoning itself.