"""AgentPulse — pipeline entrypoint. Usage: python main.py # Run the full collect → score → write loop once python main.py --collect-only # Run collectors, skip scoring python main.py --score-only # Recompute scores from existing signals python main.py --reproduce # Reproduce the paper's headline result (Table 3) """ import argparse import logging import sys logging.basicConfig( level=logging.INFO, format="%(asctime)s [%(levelname)s] %(name)s: %(message)s", ) log = logging.getLogger("agentpulse") def collect(): """Collect 18 signals across the agent registry.""" from collectors.agent_signals import AgentSignalCollector from collectors.agent_benchmarks import AgentBenchmarkCollector log.info("Collecting agent benchmark signals…") AgentBenchmarkCollector().collect() log.info("Collecting agent multi-source signals…") AgentSignalCollector().collect() def score(): """Aggregate raw signals into the four-factor composite (Section 3).""" from scoring.agent_scoring_v2 import compute_agent_scores log.info("Computing four-factor composite scores…") compute_agent_scores() def reproduce(): """Reproduce the paper's headline cross-factor predictive validity result.""" from scoring.agent_scoring_v2 import reproduce_table_3 reproduce_table_3() def main(): parser = argparse.ArgumentParser( description="AgentPulse — continuous multi-signal evaluation of AI agents" ) parser.add_argument("--collect-only", action="store_true", help="Only run collectors; skip scoring") parser.add_argument("--score-only", action="store_true", help="Only recompute scores from existing signals") parser.add_argument("--reproduce", action="store_true", help="Reproduce the paper's Table 3 headline result") args = parser.parse_args() if args.reproduce: reproduce() return if args.score_only: score() return collect() if not args.collect_only: score() if __name__ == "__main__": sys.exit(main() or 0)