Agentic Maneuverability Score - Revision history

AdminIsidore at 03:06, 9 August 2025

2025-08-09T03:06:16Z

← Older revision		Revision as of 03:06, 9 August 2025
Line 1:		Line 1:
	The '''Agentic Maneuverability (AM) Score''' is a protocol for cognitive resource allocation within ~~the~~ AetherOS ~~ecosystem~~. Its primary function is to determine the "virtuousness" of deploying a specific AI agent for a specific task, given the real-time state of the system's available resources.		The '''Agentic Maneuverability (AM) Score''' is a protocol for cognitive resource allocation within [[AetherOS]]. Its primary function is to determine the "virtuousness" of deploying a specific AI agent for a specific task, given the real-time state of the system's available resources.

	The score is the output of a calculation that treats the agent, the task, and the system not as abstract software entities, but as components in a single cyber-physical system. It grounds abstract computational requests in the concrete realities of supply and demand, drawing direct inspiration from the principles of [[Energy–maneuverability theory\|Energy-Maneuverability Theory]].		The score is the output of a calculation that treats the agent, the task, and the system not as abstract software entities, but as components in a single cyber-physical system. It grounds abstract computational requests in the concrete realities of supply and demand, drawing direct inspiration from the principles of [[Energy–maneuverability theory\|Energy-Maneuverability Theory]].

AdminIsidore at 19:51, 8 August 2025

2025-08-08T19:51:19Z

← Older revision		Revision as of 19:51, 8 August 2025
Line 1:		Line 1:
	'''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 '''Agentic Maneuverability (AM) Score''' is a protocol for cognitive resource allocation within the AetherOS ecosystem. Its primary function is to determine the "virtuousness" of deploying a specific AI agent for a specific task, given the real-time 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 score is the output of a calculation that treats the agent, the task, and the system not as abstract software entities, but as components in a single cyber-physical system. It grounds abstract computational requests in the concrete realities of supply and demand, drawing direct inspiration from the principles of [[Energy–maneuverability theory\|Energy-Maneuverability Theory]].

			== Core Philosophy: The Battlecruiser and the F-16 ==
			The AM Score is not a measure of whether a model is "good" or "bad." It is a measure of '''appropriateness'''. A battlecruiser is not a failed F-16; it is a different class of vessel with a different set of capabilities and resource requirements.

			The AM protocol is designed to prevent the misuse of agents by ensuring the system only commits to actions that are '''virtuous'''. A virtuous action is one where the capabilities of the agent are well-matched to the demands of the task, and the capacity of the system is sufficient to support the effort without compromising its own integrity.

	== The OODA Loop Framework ==		== 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~~.		The AM Score protocol is a direct implementation of the '''[[OODA Loop]]''', a strategic framework for rational decision-making in dynamic environments.
	* '''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'''.		* '''Observe''': The system gathers real-time data on three components:
	* '''Orient''': The system applies the Universal Agentic Maneuverability Equation to synthesize the observed data into a single, coherent metric: the Task Load.		** The '''[[System Maneuverability Score]]''' (Y): The available computational and energy capacity of the host system.
	* '''Decide''': The final Virtuousness Score (System Maneuverability / Task Load) provides the basis for a clear, threshold-based decision.		** The '''Agent''' (Z): The intrinsic characteristics of the selected AI model, drawn from the [[Agentic Maneuverability Almanac]].
			** The '''Task''' (X): The projected demands of the proposed mission, drawn from a registry of task profiles.
			* '''Orient''': The system applies the Universal Agentic Maneuverability Equation to synthesize the observed data into a single, coherent metric: the '''Task Load'''. This orients the system by providing a unified understanding of the mission's cost.
			* '''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).		* '''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 ==		== 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.		The core of the protocol is the '''Universal AM Equation''', which calculates a projected '''Task Load'''—a dimensionless quantity representing the holistic stress a given mission will place on the system.

	=== Equation v2 ===		=== Equation v2 ===
Line 20:		Line 28:

	Where:		Where:
	* '''P''' is the size of the agent's underlying model in '''billions of parameters'''.		* '''P''' is the size of the agent's underlying model in '''billions of parameters'''. This represents the agent's "cognitive mass" or inertia.
	* '''W_p''' is the '''Parameter Weight''', a tunable constant that scales the influence of model size.		* '''W_p''' is the '''Parameter Weight''', a tunable constant of the ecosystem that scales the influence of model size.
	* '''T_o''' is the '''Expected Output Tokens''', representing the anticipated ~~length~~ of the ~~agent's response~~.		* '''T_o''' is the '''Expected Output Tokens''', representing the anticipated volume of cognitive work required by the task.
	* '''W_t''' is the '''Token Weight''', a constant scaling the cost of generating ~~tokens~~.		* '''W_t''' is the '''Token Weight''', a constant scaling the cost of generating each unit of output.
	* '''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").		* '''F_c''' is the '''Cognitive Load Factor''', a multiplier derived from a qualitative assessment of the task's intrinsic difficulty (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.		* '''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 or "thrust."
	* '''D_v''' is the '''Velocity Dampener''', a constant that prevents division by zero and smooths the impact of velocity.		* '''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:		The final '''Virtuousness Score''' is then calculated by comparing the system's capacity to the projected load:
	<math>		<math>
	\text{Virtuousness} = \frac{\text{System Maneuverability}}{\text{Task Load}}		\text{Virtuousness} = \frac{\text{System Maneuverability}}{\text{Task Load}}
	</math>		</math>

	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.		A score '''greater than 1.0''' indicates a virtuous action, meaning the system has ample capacity to handle the load efficiently. A score '''less than 1.0''' indicates the action will stress the system, consuming more resources than are sustainably available and potentially leading to degraded performance or instability.

			{{AM_Navigation}}

AdminIsidore: Created page with "'''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 su..."

2025-08-08T17:54:16Z

Created page with "'''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 su..."

New page

'''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:
<math>
\text{Task Load} = \frac{ ( (P \times W_p) + (T_o \times W_t) ) \times F_c }{ \sqrt{V_c} + D_v }
</math>

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:
<math>
\text{Virtuousness} = \frac{\text{System Maneuverability}}{\text{Task Load}}
</math>

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.