{"id":"https://openalex.org/W7164898166","doi":"https://doi.org/10.48550/arxiv.2606.16465","title":"When Agent Automation Becomes Profitable: Quantifying and Insuring Autonomous AI Risk through Trace-Economic Underwriting","display_name":"When Agent Automation Becomes Profitable: Quantifying and Insuring Autonomous AI Risk through Trace-Economic Underwriting","publication_year":2026,"publication_date":"2026-06-15","ids":{"openalex":"https://openalex.org/W7164898166","doi":"https://doi.org/10.48550/arxiv.2606.16465"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.16465","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16465","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.16465","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138715307","display_name":"Binyan Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Binyan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138709276","display_name":"Xilin Dai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dai, Xilin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138750124","display_name":"Fan Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Fan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5008237643","display_name":"Kehuan Zhang","orcid":"https://orcid.org/0000-0003-1519-0057"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Kehuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.15770000219345093,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.15770000219345093,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.11879999935626984,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10525","display_name":"Human-Automation Interaction and Safety","score":0.07580000162124634,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/underwriting","display_name":"Underwriting","score":0.680899977684021},{"id":"https://openalex.org/keywords/audit","display_name":"Audit","score":0.6432999968528748},{"id":"https://openalex.org/keywords/automation","display_name":"Automation","score":0.5519999861717224},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.4950999915599823},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.4564000070095062},{"id":"https://openalex.org/keywords/scope","display_name":"Scope (computer science)","score":0.42410001158714294},{"id":"https://openalex.org/keywords/risk-management","display_name":"Risk management","score":0.41760000586509705},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.41449999809265137}],"concepts":[{"id":"https://openalex.org/C26503482","wikidata":"https://www.wikidata.org/wiki/Q1898080","display_name":"Underwriting","level":2,"score":0.680899977684021},{"id":"https://openalex.org/C199521495","wikidata":"https://www.wikidata.org/wiki/Q181487","display_name":"Audit","level":2,"score":0.6432999968528748},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5611000061035156},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.5519999861717224},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.4977000057697296},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.4950999915599823},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.4564000070095062},{"id":"https://openalex.org/C2778012447","wikidata":"https://www.wikidata.org/wiki/Q1034415","display_name":"Scope (computer science)","level":2,"score":0.42410001158714294},{"id":"https://openalex.org/C32896092","wikidata":"https://www.wikidata.org/wiki/Q189447","display_name":"Risk management","level":2,"score":0.41760000586509705},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.41449999809265137},{"id":"https://openalex.org/C90329073","wikidata":"https://www.wikidata.org/wiki/Q914232","display_name":"Ask price","level":2,"score":0.3995000123977661},{"id":"https://openalex.org/C27504089","wikidata":"https://www.wikidata.org/wiki/Q2602080","display_name":"Audit risk","level":3,"score":0.3935000002384186},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.33980000019073486},{"id":"https://openalex.org/C2780310539","wikidata":"https://www.wikidata.org/wiki/Q12547192","display_name":"Imperfect","level":2,"score":0.32409998774528503},{"id":"https://openalex.org/C101846000","wikidata":"https://www.wikidata.org/wiki/Q56277706","display_name":"Credence good","level":3,"score":0.31529998779296875},{"id":"https://openalex.org/C162118730","wikidata":"https://www.wikidata.org/wiki/Q1128453","display_name":"Actuarial science","level":1,"score":0.29109999537467957},{"id":"https://openalex.org/C13687954","wikidata":"https://www.wikidata.org/wiki/Q4826847","display_name":"Autonomous agent","level":2,"score":0.28529998660087585},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.27559998631477356},{"id":"https://openalex.org/C2777834853","wikidata":"https://www.wikidata.org/wiki/Q96776939","display_name":"Liability","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.27239999175071716},{"id":"https://openalex.org/C2984055962","wikidata":"https://www.wikidata.org/wiki/Q162714","display_name":"Default risk","level":3,"score":0.26249998807907104},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.2515999972820282}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.16465","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16465","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.16465","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.16465","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"AI":[0,46],"agents":[1],"can":[2,48],"now":[3],"take":[4],"irreversible":[5],"actions":[6],"in":[7],"operational":[8],"systems,":[9],"but":[10],"agent-caused":[11],"losses":[12],"are":[13,27],"still":[14],"not":[15],"clearly":[16],"assigned,":[17],"priced,":[18],"or":[19],"transferred.":[20],"Providers":[21],"often":[22],"disclaim":[23],"consequential":[24],"damages,":[25],"users":[26],"left":[28],"with":[29,91],"uncompensated":[30],"losses,":[31],"and":[32,66,83,94,108,118,144,177],"default":[33],"human":[34],"review":[35],"limits":[36],"the":[37,62,79],"efficiency":[38],"gains":[39],"of":[40],"automation.":[41],"We":[42,97,173],"ask":[43],"when":[44,74],"autonomous":[45],"deployment":[47],"become":[49],"economically":[50],"acceptable":[51,73],"despite":[52],"failure":[53],"risk.":[54,85],"Our":[55],"answer":[56],"is":[57,72],"to":[58,105,142],"quantify":[59],"risk":[60,119],"at":[61],"customer-task-trace":[63],"episode":[64],"level":[65],"transfer":[67],"it":[68],"through":[69],"insurance.":[70],"Automation":[71],"its":[75],"expected":[76],"benefit":[77],"exceeds":[78],"premium,":[80],"control":[81],"cost,":[82],"remaining":[84],"This":[86],"requires":[87],"a":[88,169],"defined":[89],"role":[90],"bounded":[92],"permissions":[93],"comparable":[95],"traces.":[96],"introduce":[98],"trace-economic":[99,135],"underwriting,":[100],"which":[101],"maps":[102],"tool-use":[103],"traces":[104],"customer":[106],"exposure":[107],"claimable":[109],"loss,":[110],"then":[111],"uses":[112,122],"this":[113],"representation":[114],"for":[115],"pricing,":[116],"control,":[117],"transfer.":[120],"It":[121],"deterministic":[123],"economic":[124],"labels":[125,154],"rather":[126],"than":[127],"an":[128],"LLM":[129],"judge.":[130],"In":[131],"our":[132],"trace-to-loss":[133],"testbed,":[134],"pricing":[136,138],"reduces":[137],"MAE":[139],"from":[140],"$17.7K":[141],"$569":[143],"removes":[145],"regressive":[146],"cross-subsidy.":[147],"A":[148],"300-trace":[149],"expert":[150],"audit":[151,178],"accepts":[152],"295":[153],"unchanged.":[155],"On":[156],"1,000":[157],"real":[158],"SWE-smith":[159],"traces,":[160],"trace-conditioned":[161],"controls":[162],"reduce":[163],"CVaR95":[164],"by":[165],"72%.":[166],"Theorem~1":[167],"gives":[168],"finite-sample":[170],"scope":[171],"condition.":[172],"release":[174],"code,":[175],"labels,":[176],"sheets.":[179]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-17T00:00:00"}