[FEATURE] Implement Self-Evolving Cognitive Agent with Red-Blue Team Adversarial Learning (EvoMail/COG)

[BhakktiGautam]

Labels: feature, SSoC26, hard, ai

##Description
EvoMail (COG) is a self-evolving cognitive agent framework that mimics human analysts . It uses:

Red-team agent: Generates novel evasion tactics
Blue-team detector: Learns from failures and adapts
Memory module: Compresses experiences for future reasoning

##Why This Matters
Continuous Adaptation: Self-evolves to counter new threats
Red-Blue Team: Simulates attacks to strengthen defense
Cognitive Reasoning: Mimics human analysts

##Suggested Fix
python

backend/evo_mail.py

class CognitiveAgent:
def init(self):
self.memory = [] # Compressed experiences
self.red_team = AdversarialGenerator()
self.blue_team = SpamDetector()
self.coggcn = CognitiveGNN() # LLM-enhanced GNN

def evolve(self):
    # Red team generates new spam variants
    attacks = self.red_team.generate_attacks()
    
    # Blue team learns from failures
    self.blue_team.train(attacks)
    
    # Compress experience into memory
    self.compress_experience()
    
    # Use memory for future reasoning
    return self.coggcn.reason(self.memory)

[github-actions]

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[github-actions]

The proposal discusses implementing a framework called EvoMail (COG), a self-evolving cognitive agent designed to mimic human analysts in detecting and adapting to new threats. It employs a red-team agent to generate novel attack tactics, a blue-team detector to learn and adapt to these tactics, and a memory module to compress experiences for improved future reasoning. The approach aims to provide continuous adaptation, simulate adversarial scenarios for stronger defenses, and enable cognitive reasoning.