u/EddyHKG — reddlx

The Agentic AI Circular Flow Model – Addressing Recursive Risk and Operational Instability

Most AI governance treats models like static tools, but Agentic AI is a loop.

As shown in the diagram, once an agent hits Stage 4 (Action), it changes its own environment, creating Compounding Risk that humans can't track in real-time.

I just published a paper on SSRN proposing a new framework to handle this operational instability. It moves the conversation from "better prompting" to deterministic infrastructure.

Key focus: Why Stage 4 is the "point of no return" for autonomous systems.

Read the full framework on SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract\_id=6425138

u/EddyHKG — 22 hours ago

▲ 0 r/cybersecurity

A Circular Flow Model for Agentic AI: Managing Recursive Risk and Operational Instability

As we move from linear AI tools to autonomous agents, we face new "recursive risks" where small errors can spiral into system-wide instability.

My paper introduces a Circular Flow Model to visualize these loops and proposes four specific guardrails (Structural, Execution, Memory, and Assurance) to maintain control.

Thought this might be useful for anyone building or securing agentic workflows in enterprise environments.

papers.ssrn.com

u/EddyHKG — 1 day ago

▲ 1 r/AI_Agents

The "Circular Flow" Framework: Managing Recursive Risk in AI Agents

Most risk models are linear, but agents are recursive: Input → Model → Output → Action → New Input.

This creates Circular Risk, where an agent’s minor errors compound through every loop, leading to "behavioral drift" and operational instability.

To solve this, I’m sharing a four-layer guardrail framework:

Structural:Hard-coded environmental and tool-access boundaries.

Execution: Real-time "circuit breakers" for the decision-to-action pipeline.

Memory: Filtering agent recollections to prevent self-reinforcing errors.

Assurance: Continuous verification of the loop against the original intent.

reddit.com

u/EddyHKG — 3 days ago