AgentRank-A2A

# AgentRank: Intelligent Agent Selection Layer for A2A Ecosystems *A domain-aware, learning, exploration-enabled ranking engine for multi-agent systems.* ## Overview Modern multi-agent environments often contain **multiple agents that provide similar capabilities** (e.g., summarizers, translators, recruiters). The A2A protocol defines how agents communicate, but it does **not** define how to select the best agent. **AgentRank** solves this by ranking agents using: - performance metrics - domain policies - exploration techniques - learning from logs ## Features - Intelligent agent selection - Config-driven scoring - Domain-aware policies - UCB exploration - Dynamic learning - A2A integration ## Architecture ``` Client Agent → AgentRank Service → Best Agent → A2A Request → Logs ``` ## Metrics - **Success Rate (SR)** - **Quality Score (QS)** - **Latency Score (LS)** - **Failure Rate (FR)** ## Ranking Algorithm ### 1. Base Score ``` base_score = wSR·SR + wQS·QS + wLS·LS + wFR·FR ``` ### 2. Exploration Bonus (UCB) ``` exploration = α * sqrt( ln(1+N) / (1+n_a) ) ``` ### 3. Final Score ``` final_score = base_score + exploration ``` ## Project Structure ``` │ run_demo.py │ agent_client.py │ agent_rank_service.py │ log_store.py │ domain_registry.py │ a2a_protocol.py └ agents/ ``` ## Running ``` python run_demo.py ``` ## Conclusion AgentRank transforms a static multi-agent system into a **self-optimizing, intelligent, scalable ecosystem**.