AASI: The Agent-Anchored Semantic Index

As Artificial General Intelligence (AGI) increasingly emerges from distributed networks of specialized agents ("Patchwork AGI"), the mechanism for agent discovery becomes critical infrastructure. Traditional approaches rely on Semantic Search (S) for relevance and Decentralized Reputation (P) for quality. In this paper, we identify and formalize the "Agent Discovery Trilemma", demonstrating that systems optimizing only for S and P are structurally vulnerable to AI-driven Sybil attacks. Through a trace-driven simulation of 1,000 agents and 100 rounds of interaction, we empirically show that adversarial agents using LLM-optimized SEO and collusion rings can capture nearly 100% of top-10 search results. We demonstrate that defense requires a Two-Stage Mechanism: (1) Broad Visibility (K_retrieval >> N_sybil) to prevent index flooding, and (2) The Defense Inequality via Identity Anchoring (T) to suppress deceptive rankings. We propose the Agent Anchored Semantic Index (AASI), a hybrid architecture that enforces these conditions. Our results show that while baseline configurations collapse under attack (Sybil@10 = 0.993), enforcing the Two-Stage Defense reduces Sybil dominance to 0.000, confirming our hypothesis that Computational Staking, when coupled with expanded retrieval visibility, is required to achieve stability in our evaluated regime.