{"id":"W4407776740","doi":"10.20944/preprints202502.1647.v1","title":"Agentic AI: A Quantitative Analysis of Performance and Applications","year":2025,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Discovery Centre","funders":"","keywords":"Artificial intelligence; Computer science; Psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005515499,0.0001969152,0.0004912686,0.0006161232,0.0000950535,0.00003566159,0.0008372074,0.0001458429,0.00004276109],"category_scores_gemma":[0.00004950705,0.0002025072,0.0001897294,0.0009024687,0.00005487086,0.0001716245,0.001743616,0.0002683931,0.0000616936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005532371,"about_ca_system_score_gemma":0.0001167042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001985864,"about_ca_topic_score_gemma":0.00003192665,"domain_scores_codex":[0.9980878,0.0001203681,0.0005426145,0.0008204795,0.0002665986,0.0001621159],"domain_scores_gemma":[0.997764,0.0001047112,0.000446907,0.001381899,0.0002382002,0.00006427931],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000008848861,0.0001600271,0.9359127,0.0009557313,0.001633971,8.261912e-7,0.003483155,0.01172337,0.001312593,0.04176702,0.00001870072,0.003023],"study_design_scores_gemma":[0.0001164808,0.000008064376,0.7208692,0.0001271256,0.0004067438,3.957991e-7,0.00002823774,0.2744024,0.00221412,0.0004350236,0.001202544,0.0001897462],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4868999,0.0001823746,0.5091919,0.0001475273,0.0001836076,0.0007274926,0.00003094754,0.00008552198,0.002550721],"genre_scores_gemma":[0.9957467,0.0002234865,0.002781996,0.00005182322,0.00001866211,0.0002834726,0.00003055157,0.000005000478,0.0008583007],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5088469,"threshold_uncertainty_score":0.8258004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1297349774182158,"score_gpt":0.3772071575737725,"score_spread":0.2474721801555567,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}