I have a proposed architecture that blocks adversarial drift in agentic AI systems. It decouples persistent state from conversational context and enforces deterministic turn isolation. A summary is below. Happy to provide more details to anyone qualified to help with this.

The Problem:

Current AI agents are vulnerable to a class of attack that exploits their reliance on conversational continuity. An adversary doesn't need to break security controls in a single turn—they can gradually steer the agent toward compromising actions over dozens or hundreds of interactions. Each turn adds subtle pressure, and because the model's attention mechanism weights recent context more heavily, the original system prompt's authority degrades over time. This cumulative semantic drift is the root failure mode of every existing agentic system. It's not a bug in any specific model—it's a structural vulnerability in how agents are built.

The Solution:

The architecture solves this by decoupling persistent state from conversational context. Objectives, tool permissions, and value hierarchies are stored outside the context window, versioned, and cryptographically signed. Each inference is treated as an isolated, stateless transaction—the LLM receives only a read-only snapshot of the current state, not the history that would allow drift to accumulate. Any modification to that state requires an authenticated, out-of-band transaction; the agent cannot be talked into changing its own directives. The result is an agent that remains predictable and controllable regardless of adversarial input, with drift monitoring providing a secondary layer of defense.