Agentic Maneuverability Score

Agentic Maneuverability (AM) is a protocol for decision-making in an AI ecosystem. Its primary function is to determine the "virtuousness" of deploying a specific AI agent for a specific task, given the current state of the system's available resources.

The score is the output of a calculation that treats the agent, the task, and the system as components in a single physical system, grounding abstract software requests in the concrete realities of computational supply and demand.

The OODA Loop Framework

The AM Score protocol is an implementation of the OODA Loop (Observe, Orient, Decide, Act), a strategic framework for rational decision-making under pressure.

• Observe: The system gathers real-time data on the System Maneuverability Score (the available computational resources), the intrinsic characteristics of the selected AI Agent, and the demands of the proposed Task.
• Orient: The system applies the Universal Agentic Maneuverability Equation to synthesize the observed data into a single, coherent metric: the Task Load.
• Decide: The final Virtuousness Score (System Maneuverability / Task Load) provides the basis for a clear, threshold-based decision.
• Act: A user or automated system acts on the decision, proceeding with the mission only if the action is deemed virtuous (i.e., the score is sufficiently high).

Technical Description

The core of the protocol is the Universal AM Equation, which calculates a projected Task Load—a measure of the stress a given mission will place on the system.

Equation v2

The second iteration of the equation is defined as: $\text{Task Load}=\frac{((P\times W_p)+(T_o\times W_t))\times F_c}{\sqrt{V_c}+D_v}$

Where:

• P is the size of the agent's underlying model in billions of parameters.
• W_p is the Parameter Weight, a tunable constant that scales the influence of model size.
• T_o is the Expected Output Tokens, representing the anticipated length of the agent's response.
• W_t is the Token Weight, a constant scaling the cost of generating tokens.
• F_c is the Cognitive Load Factor, a multiplier derived from a qualitative assessment of the task's cognitive demands (e.g., "low", "medium", "high").
• V_c is the agent's Cognitive Velocity, measured in tokens per second (tps) on a baseline hardware profile. This represents the agent's raw processing speed.
• D_v is the Velocity Dampener, a constant that prevents division by zero and smooths the impact of velocity.

The final Virtuousness Score is then calculated as: $\text{Virtuousness}=\frac{\text{System Maneuverability}}{\text{Task Load}}$

A score greater than 1.0 indicates a virtuous action, meaning the system has ample capacity to handle the load. A score less than 1.0 indicates the action will stress the system, consuming more resources than are sustainably available.