TL;DR Securing AI agents is not just a prompt engineering problem. It is a systems engineering problem involving latency, execution control, architectural isolation, and trust boundaries. Stacking multiple LLM-based guardrails naively can quickly destroy responsiveness. Strong security pipelines must balance protection, latency, infrastructure cost, and usability together. Lightweight computational filters are still valuable because they cheaply absorb noisy attacks before expensive reasoning layers are triggered. Context isolation and execution controls matter more than endlessly adding smarter classifiers. A compromised model should not automatically gain authority to execute sensitive actions. The goal is not perfect prevention. It is building systems where successful injections have limited influence, limited execution power, and limited blast radius. In the previous blog, we talked about why prompt injection is fundamentally an architectural problem.…