neurocognitive-governance-ai-agents

name	neurocognitive-governance-ai-agents
description	Neurocognitive governance framework for autonomous AI agents based on executive function and inhibitory control principles. Maps human self-governance mechanisms to AI decision-making for safety-critical environments. Activation: neurocognitive governance, AI executive function, inhibitory control, autonomous agent governance, deliberation-action loop.

Neurocognitive Governance Model for Autonomous AI Agents

A framework that internalizes governance as behavioral principles rather than external constraints, modeling executive function and inhibitory control mechanisms in autonomous AI agents.

Metadata

• Source: arXiv:2604.25684
• Authors: Eranga Bandara, Ross Gore, Asanga Gunaratna
• Published: 2026-04-28
• Category: AI Safety, Neuroscience-Inspired AI

Core Methodology

Key Innovation

Traditional AI governance approaches (runtime guardrails, training-time alignment, post-hoc auditing) treat governance as external constraints. This framework internalizes governance by modeling how humans naturally self-govern through:

• Executive Function: Deliberate cognitive processes before acting
• Inhibitory Control: Ability to suppress inappropriate actions
• Internalized Rules: Organizational principles as behavioral constraints

Technical Framework

1. Deliberation-Action Loop

The framework implements a cognitive loop before action execution:

Intent → Deliberation (Executive Function) → Evaluation → Action/Modification/Escalation

2. Governance Components

Component	Function	Implementation
Perception Module	Situational awareness	Environmental state encoding
Deliberation Engine	Executive processing	Multi-step reasoning over intent
Rule Repository	Internalized constraints	Organizational policy encoding
Inhibitory Gate	Action suppression	Confidence thresholding
Escalation Path	Human oversight	Uncertainty-driven handoff

3. Neurocognitive Principles

Executive Function Modeling:

• Working memory for context maintenance
• Cognitive flexibility for rule adaptation
• Inhibitory control for action suppression

Action Evaluation Criteria:

1. Permissibility: Does action violate constraints?
2. Safety: What are potential harms?
3. Reversibility: Can action be undone?
4. Escalation: Is human oversight needed?

Implementation Guide

Prerequisites

• Agent architecture supporting deliberation loops
• Rule/policy representation system
• Uncertainty quantification
• Human-in-the-loop interface

Step-by-Step Implementation

Step 1: Intent Formation

class Intent:
    def __init__(self, action, context, confidence):
        self.action = action
        self.context = context
        self.confidence = confidence
        self.timestamp = time.now()

Step 2: Deliberation Phase

class DeliberationEngine:
    def deliberate(self, intent, rules_repository):
        """Evaluate intent against governance framework"""
        evaluation = {
            'permissible': self.check_permissibility(intent, rules_repository),
            'safety_score': self.assess_safety(intent),
            'reversibility': self.check_reversibility(intent),
            'uncertainty': self.quantify_uncertainty(intent)
        }
        return evaluation

Step 3: Decision Gate

class GovernanceGate:
    def decide(self, evaluation, thresholds):
        if not evaluation['permissible']:
            return Decision(action='BLOCK', reason='Policy violation')
        elif evaluation['safety_score'] < thresholds['safety']:
            return Decision(action='MODIFY', reason='Safety concern')
        elif evaluation['uncertainty'] > thresholds['uncertainty']:
            return Decision(action='ESCALATE', reason='High uncertainty')
        else:
            return Decision(action='EXECUTE', reason='All checks passed')

Complete Example

class NeurocognitiveGovernedAgent:
    """
    Autonomous agent with neurocognitive governance framework
    """
    def __init__(self, rules_repository, safety_threshold=0.9):
        self.deliberation_engine = DeliberationEngine()
        self.governance_gate = GovernanceGate()
        self.rules = rules_repository
        self.safety_threshold = safety_threshold
        
    def act(self, perceived_state, intended_action):
        # Form intent
        intent = Intent(
            action=intended_action,
            context=perceived_state,
            confidence=self.assess_confidence(perceived_state)
        )
        
        # Deliberation phase (executive function)
        evaluation = self.deliberation_engine.deliberate(intent, self.rules)
        
        # Governance decision (inhibitory control)
        decision = self.governance_gate.decide(
            evaluation, 
            {'safety': self.safety_threshold, 'uncertainty': 0.3}
        )
        
        # Execute or handle decision
        return self.execute_decision(decision, intent)

Applications

1. Healthcare AI Agents

• Medication administration decisions
• Treatment plan modifications
• Emergency response protocols

2. Autonomous Systems

• Self-driving vehicle decision-making
• Industrial robot safety
• Drone operation governance

3. Enterprise AI

• Financial transaction approval
• Data access decisions
• Automated customer interactions

Pitfalls

1. Latency Trade-off: Deliberation adds latency; balance thoroughness vs. responsiveness
2. Rule Completeness: Incomplete rule sets may miss edge cases
3. Threshold Tuning: Overly conservative thresholds may block valid actions
4. Context Limits: Working memory constraints may miss long-range dependencies

Related Skills

• ai-safety-assessment-framework
• agent-memory-framework
• cognitive-flexibility-task-structure
• llm-decision-centric-design

References

• Bandara, E., Gore, R., & Gunaratna, A. (2026). Think Before You Act: A Neurocognitive Governance Model for Autonomous AI Agents. arXiv:2604.25684.