美银-互联网电子商务行业概述：智能体AI与更强大互联网的前景-Agentic AI and the promise of a more capable Internet-20260528

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BofAGOBALRESEARCHBofASE

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BofASecuritiesdoesandseekstodobusinesswithissuerscoveredinitsresearch

reports.Asaresult,investorsshouldbeawarethatthefirmmayhaveaconflictof

interestthatcouldaffecttheobjectivityofthisreport.Investorsshouldconsiderthisreportasonlyasinglefactorinmakingtheirinvestmentdecision.

Refertoimportantdisclosuresonpage28to30.AnalystCertificationonpage26.Price

ObjectiveBasis/Riskonpage24.12978772

Internet/e-Commerce

AgenticAIandthepromiseofamorecapableInternet

IndustryOverview

TheAgenticEramovingfromconcepttoreality

TheemergenceofChatGPT,Gemini,andGoogleAIModeischanginghowconsumers

interactwiththeInternet,withconversationalAIincreasinglybecomingaprimary

interfacefordiscoveryanddecision-making.AgenticAIwillbuildonthisevolution,

advancingInternetgateway(search)capabilitiesfrominformationretrievaltoevaluationandautomatedtaskexecution.Wethinkofagentsasanembeddedproductivity

softwaretoolforconsumers,withthepotentialtosignificantlyenhancedecisionqualitywhilereducingtransactionfriction.Givenrapidlychanginguserbehaviorand

expectations,wethinktransaction-basedverticals,suchaseCommerce,Onlinetravel,andfinance,willneedtobuildtheirownfront-endAIchatanddownstreamAgenticcapabilitiestomaintaintheirrelevancewithconsumers.

Marketplaceswillstilloffersignificantconsumervalue

AIcannotsecuredifferentiatedsupply,deliverproductsinonehour,orpassonnetwork

effectcostsavings.AsusersbecomemoreaccustomedtoAIinteractions(GoogleAIModenowhas1bnusers),OnlinetravelandeCommercemarketplaceswillneedtofocusontwoareas:1)Competitivefront-endAIexperiencestomaintaindirecttraffic,and2)Maximizingproductdifferentiation,especiallyinthephysicalworld.Wethinkthebestselection,prices,shippingspeedsandcustomerserviceshouldbeequallyattractivetoagentsastheyare

toconsumers.Whileproducerpricingsurplusescouldbeatrisklong-term,verticalsitescanleveragetheircompetitiveadvantagesinsupplyaggregation,fulfilmentspeed,

customerservice,and/orcostsavingsfromnetworkeffectstodrivedirectagentictraffic.

Newadformats&toolstoreshapedigitaladvertising

Recently,GoogleI/OandMarketingLiveprovidedanearlylookatseveralAI-related

digitaladvertisingproductsthateitherleverageAIusecases,oraredesignedtoenableAgentictransactioncapabilities.Threenotableinitiativesincluded:1)ConversationalAIadsthatintegratedirectlywithinAI-generatedresponses,allowingadvertiserstoengageusersincontextwithtailoredmessaging,2)AIpoweredshoppingadsthatsurface

relevantproductsalongsideAI-generatedexplanationstoguideevaluationanddecision-making,and3)UniversalshoppingcartthatenablesuserstoaddandpurchaseproductsdirectlywithinGooglesites.Byintegratingreal-timeproductdata,capturingricher

intent,andenablingtransactions,webelieveagenticsystemsshouldimproveuser

targeting,conversionandmeasurement,leadingtoincrementaluseractivityandspend.

Googleinthepolepositionfortheagenticera

WethinkGoogleiswellpositionedtoleadinagenticadoptiongivenleadingfoundationmodels,agentictimetomarketadvantages,anddeepmerchantdata.Google’searly

moveradvantagecouldhelpestablishecosystemlock-inandguidebehavioraldefaults

aroundagenticworkflows.Moreover,Google’sadvertiserecosystemcouldseeupside

fromhigherAIdrivenvolumes,adclickratesandadtargeting(seepage17).Maintain

Buy.OpenAI,MetaandAmazonalsohavelargeconsumeraudiencesanddeepAI

capabilitiestoleverage,andweexpectstronginnovationandcompetitionacrossagenticecosystemsoverthenexttwoyearsastheseplatformsfocusonscalingmonetization.

28May2026

Equity

UnitedStates

JustinPost

ResearchAnalystBofAS

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justin.post@

NitinBansal,CFA

ResearchAnalystBofAS

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nbansal7@

3P:ThirdParty

AI:ArtificialIntelligence

Gen-AI:GenerativeAI

MAU:MonthlyActiveUsers

UI:UserInterface

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AIreshapingconsumerbehavior

TheemergenceofGenerativeAIsitesandevolutionofAISearchisdrivingastructuralshiftinconsumerbehavior,withAI-nativeinterfacesemergingastheprimarygatewayforconsumertrafficandreshapinghowusersdiscover,evaluate,andtransactonline.

ChatGPThasscaledto~900mnweeklyactiveusersandprocessesbillionsofqueries

daily,whileGooglehasre-architecteditscoreSearchproductaroundAI,withAI

Overviewsgrowingto~2.5bnmonthlyusers,AIModeto1bnmonthlyusersandAIModequeryvolumesmorethandoublingeachquartersincelaunch.

WebelievethegrowingprevalenceofAI-driveninteractionshasthepotentialtodriveafundamentalshiftattheconsumerlevel,withusersincreasinglyrelyingonAIsurfacestosynthesizeinformation,comparealternatives,andguidedecisionsinrealtime.The

entireInternetecosystem,frominformationtotransactionsites,willneedtoevolveuserinterfacesasconsumerdecisionmakingincreasinglyreliesonAIdrivenresults.

AgenticAI&thenexteraofInternetutility

WhatisAgenticAI?

AgenticAIrepresentsthenextevolutionoftheInternet,capableofindependently

planning,decision-making,andexecutingmulti-stepworkflowstocompleteuser-definedgoals.Unlikefirst-generationAIassistantsthatprimarilygenerateanswers,summarizeinformation,orsurfacelinksinresponsetoprompts,agenticsystemsfocusontask

completion.EffectAgenticAIassistantswillbeabletobreakhigh-levelobjectivesintosmallersteps,evaluatetrade-offsbasedonuserpreferencesorconstraints,andinteractwithexternaltoolsorservicestocarryoutactionssuchaspurchasingproducts,bookingtravel,schedulingmeetings,completingworkflows,etc.

Exhibit1:AgenticAIassistantsfocusontaskcompletionAIAssistantsvsAgenticAssistants

AIAssistants

AgenticAssistants

Primarilyretrieve,rank,orsummarizeinformationUserremainsdecision-makerandexecutor

Monetizationcenteredonimpressions,clicks,andfeedengagement

Valuetiedtotrafficreferralanddiscovery

Interpretsintentandtranslatesitintoexecutableworkflows

Comparesoptions,negotiatesconstraints,andcompletestransactions

Canbrowse,fillforms,book,purchase,schedule,orsubscribeShiftsvaluefromtrafficgenerationtooutcome

Source:BofAGlobalResearch

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Potentialconsumeragenticadoptiontimelines

TheInternetiswellintotheadoptioncycleoffirst-generationAIassistants,while

simultaneouslyenteringtheexperimentationstageforagenticsystems.Overthepastyear,consumerAIusagegrowthhasbeendrivenbychat-basedAIassistantsthat

summarizeinformation,answerquestions,assistwithcoding,andaugmentsearch.Theevolutiontowardfullyagenticsystemswillbeamulti-yearprocess(weareclosetoyear30ofeCommerceandstillonly20%USpenetration)astechnologyimprovesand

consumersgraduallyadopttonewAIcapabilities.

•First-generationAIassistants(Startedin2023):Duringthisphase,AI

assistantsbecamewidelyadoptedasproductivityandknowledgetools.Mostusagecentersonchat-basedinteractions.Personalizationandreal-world

executionislimited.

•Earlyagenticexperimentation(startedinlate2025):Assistantsbeginincorporatingtooluseandlimitedexecutioncapabilities,butsupervision

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remainsessential.SystemscanaccessexternalAPIs,retrievestructureddata,

andcompleteboundedworkflowssuchasbookingreservationsordrafting

emails,etc.

•Structuredmulti-steptaskexecution(expectedbylate2026orearly

2027):Agenticsystemsmatureintogoal-orientedplannerscapableofhandlingmulti-stepworkflowswithreducedsupervision.Agentscanhandlecomplex

tasks,evaluatetrade-offs,coordinateacrossmultipleservicesandmake

purchasesonbehalfofusers.Usertrustwillbegintobuild,buttransactionswilllikelyrequireuserapproval.

•Proactiveandambientagents(expectedby2030):Agentsoperateaspersistentdigitaloperators,embeddedindailylifeasadecision-makingandexecutionlayer.Theyproactivelymanagetasksandcontinuouslymonitor

context,learnfrombehavior,andactautonomouslywithinpredefinedboundaries.

AgentscansignificantlyincreaseInternetutility

Wethinkofagentsasanembeddedproductivitytoolforconsumers,withthepromisetosignificantlyincreaseInternetutilitythroughautomationandimprovingdecisionquality.

Whilethetraditionalinternetmodelrequiresuserstosearch,compare,andmanuallycompletetransactions,agentswillbedesignedtoperformthesefunctions

autonomously.Thistransitioncouldmeaningfullyimproveefficiency,decisionquality,andmarketparticipationacrossawiderangeofdigitalactivities.

Reducedfrictionandtime:Manyonlineactivitiesinvolveaseriesofmanualsteps-searchingforoptions,openingmultiplewebsites,comparingfeaturesorprices,andcompletingtransactionworkflows.Agentscanautomatethese

processesbyinterpretinguserintentandexecutingtasksend-to-end.Bycompressingtheseworkflows,agentsreducebothtimeandcognitiveload,increasingtheoverallproductivityofinternetusage.

Betterdecisionquality:Humandecision-makingonlineisconstrainedbytime,attention,andthelimitednumberofoptionsuserscanrealistically

evaluate.Agents,bycontrast,canprocesssignificantlylargerdatasets,whichallowsthemtooptimizedecisionsacrossabroadersetofvariables.

Higherlevelofpersonalization:Agentscancontinuouslylearnfromauser’spreferences,behavior,andconstraints.Overtime,theycanbuildadetailed

profilethatincorporatesfactorssuchasspendingpatterns,brandpreferences,schedulingconstraints,orqualitythresholds.Thisenablesahigherdegreeofpersonalizationandstrongalignmentwithindividualuserobjectives.

Unlockparticipationincomplexverticals:Certaincategories,suchas

insurance,financialproducts,healthcareservices,etc.,requiresubstantialefforttoevaluateduetofragmentedinformationandcomplexdecisionframeworks.

Agentscanaggregaterelevantdata,comparealternativesacrossproviders,and

guideusersthroughmulti-steptransactionprocesses.Byloweringthebarriers

associatedwithnavigatingthesemarkets,agentsmayincreaseparticipation

andimprovematchingbetweenconsumersandservices.

Consideringthesefactors,AgenticAIpenetrationandpotentialdisintermediationwillvarybycategory.

•Wethinkdigitalandservicescategoriesarestructurallywellsuitedforagentic

workflows,astransactionsarelargelydigital,productattributesare

comparable,and/ordecisionframeworksarerelativelystandardized.

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•WethinkcategoriessuchaseCommerce,ridesharing,andrestaurantand

grocerydelivery,withsignificantnetworkeffectsoreconomiesofscale,willbemoreinsulatedfromagenticdisruptionbutarewellsuitedforagentic

partnerships.Theseverticalshaveentrenchednetworkeffects,andvolumeleadershavecompetitiveadvantagesincostandservicelevels.Wethink

Generative-AIcompanieswillneedtopartnerwiththesescaledplatformstoofferthemostattractiveoptionstoconsumers.

•WeseeOnlinetravelasa“tweener”category,wellsuitedforagenticusage

giventravelplanningcomplexities,butpotentiallydifficulttodisintermediate

duetosupplycomplexities.Travelbookingscanbeacomplexdecisionprocess,andaredigitalwithlimitedfulfilmentcomplications,whichfavorsrapid

adoptionofAgenticAIusage.However,productsourcingcanbedifficult(long-tailinventorynotcommoditized),whileverticaltransactionsites(OTAs)have

scaleadvantagesforselection,price(supplierdiscounts)andcustomerservice.

WethinkOnlineTravelcompaniesneedtoevolvefront-enduserinterfaceswith

changingconsumerpreferences,partnerwithactivity&restaurantreservation

sitestooffermorecomprehensiveitineraries,andincorporateAgenticbooking

andtripmonitoringcapabilitiestomaintainrelevancewithconsumers.

AgenticAIbuildingblocks

WebelieveAgenticAIwillrepresentatransformativeshift,movingtheInternetfromaninformation-drivenmodeltoanexecution-drivenmodel.However,adoptionwilllikely

dependonseveralstructuralfactorsincluding:

•Standardprotocolsandintegration–Agentsrequiredeepintegrationwithplatforms,services,andpaymentsystems,alongsidethewidespreadadoptionofcommonprotocolsandstandardizedinterfacestoenablereliableinteractionacrossthedigitalecosystem,whichwilllikelytaketimetodevelopandscale.

•Reliability–Autonomoussystemsmustconsistentlydeliveraccurate

outcomestogainuserconfidence.Evensmallerrorratesindecision-makingortransactionexecutioncouldimpactadoption,particularlyforfinancialor

mission-criticaltasks.

•Trust–Usersmustbecomfortableallowingagentstomakedecisionsor

completetransactionsontheirbehalf.Forhigher-valueorsensitiveactivities,trustintheagent’sincentives,transparency,andalignmentwithuseroutcomeswillbecritical.

•Regulatory–Handlingpayments,personaldata,anddelegateddecision-

makingauthorityintroducesnewregulatoryandsecurityconsiderationsthatmayshapehowagentsaredeployedacrossindustries.

Expectsignificantagenticcompetition

Wethinkseveraltypesofagenticplatformscouldemerge,competingtobecomethedefaultinterfacefordigitaltasksanddecision-making:

Operatingsystem–levelagents:Agentsembeddeddirectlywithindevice

operatingsystemssuchasiOSorAndroidthatcanexecutetasksacrossappsandserviceswithinthedeviceenvironment.Astheycouldbepre-installedandsetasthedefaultonbillionsofinstalleddevicebase,theseagentscouldhavecompetitiveadvantages.

Model-layeragents:AgentsbuiltbycompaniesdevelopingfrontierAImodelsthatoperateacrossmultipleapplicationsandservicesthroughAPIsand

integrationsratherthandirectoperatingsystemcontrol.ExamplesincludeagentsfromcompaniessuchasOpenAI,Anthropic,Perplexity,Google,Meta,

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etc.thatcancoordinatetasksacrossdifferentapps,websites,andservices

fromasingleinterface.

Commerce-nativeagents:AgentsdevelopedbylargecommerceplatformssuchasAmazon,Walmart,eBay,etc.thatfocusspecificallyonshopping

decisionsandtransactionexecution.Theseagentscouldleverageproprietaryproductcatalogs,merchantnetworks,logisticsinfrastructure,andpayment

systemstoresearch,recommendandcompletepurchaseswithinasingle

platformenvironment.Theseagentswilllikelytrytoexpandtheirfunctionalityandreachbyincorporatingdataandproductsfromsmallersellersthatthe

agentscanactupon.

Vertical-specificagents:Agentsdesignedforspecificindustriessuchas

travel,finance,healthcare,orenterpriseproductivity.Thesesystemsfocusonanarrowersetofusecasesbutmayofferdeeperdomainexpertiseand

integrationwithadjacentcategories,enablingmorespecialized

recommendationsandtaskexecutionwithinaparticularsector.Forexample,Booking’stravel-focusedagent,Penny,thatfromthatcanplananentiretripbyrecommendingflights,bookinghotels,reservingrestaurants,andorganizing

localtransportationwithinasingleworkflow.

DigitaladvertisinginAgenticAIera

Asagenticcapabilitiesscale,weexpectastructuralshiftinbothusersearchbehavior

andthemonetizationofhigh-intentcommercialqueries.Agenticsystemscouldreshapethedigitalcommercejourneybycompressingresearch,evaluation,decision-making,andexecution,andtheneitherdeliversummarizedchoicestoconsumers,ortakeaction.

Whatwaspreviouslyamulti-stageuserjourneymayincreasinglyoccurwithinonecoordinatedagentinteraction.

Thisshiftwilllikelyhavesignificantimplicationsforthedigitaladvertisingecosystem

andtraditionaladvertisingfunnel.Inagenticinteractions,theupperandmidfunnel

(awarenessandconsideration)couldbeconsolidatedintotheagent’sdecisionengine,

enablingassessmentofproductsandtrade-offs,withthelowerfunnel(click,landing

page,andcheckout)completednativelywithintheplatformthroughembeddedcheckoutandtransactioncapabilities.

Traditionalflow:Search→Impression→Evaluation→Click→Browse→Compare→RefineSearch→Impression→Evaluation→Click→Compare→Clicktoaddtocart→Checkout

Agenticflow:CommunicateIntent→Agentevaluates→Agentrecommends→Userapproves→Transactionexecuted

InthetraditionalWebecosystem,economicvaluehaslargelyaccruedtoplatformsthatgenerateanddistributetraffic,andtotransactionsitesthathavehighestconversion

ratesofthattraffic.Inanagenticmodel,however,agentsmayretrieveproductdata

directlythroughAPIs,structureddatabases,ormerchantintegrations,reducingtheneedforuserstovisitmultipletraditionalwebsitesduringthedecision-makingprocess.Asaresult,economicvaluecouldshifttowardplatformsthatcontrolthedecisionengine

itself.Transactionbasedsitesthathaveproprietaryuserinformation,inventoryor

customerservicecapabilitieswillneedtocarefullynegotiatetermswithagenticsitestoensuretheymaintainfavorableeconomicsandconsumerbrandpositioninagentic

transactions.

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AgentictransformationbeingdrivenbyGoogle

AtGoogleI/O2026andMarketingLive,GoogleintroducedarangeoftransformativefeaturesandnewcapabilitieswithinSearchtodrivemoreagenticexperiencesandcommerce.Keyannouncementsincluded:

AgenticadsandNativecheckoutwithinAISearch

Googleintroducednewagenticadvertisingcapabilitiesandadformatsdesignedtomakeadsmoreinteractive.ThecompanylaunchedBusinessAgentforLeads,whichallows

userstoengagedirectlywithadvertiseragentsthroughAI-drivenpromptsandpre-filledleadworkflows.NewadformatsbuiltspecificallyforAIModeincludedconversationalAIanswerads,DirectOffers,AI-poweredShoppingAds,andretailer-connectedcommerceadsthatlinkusersdirectlytomerchantswithavailableinventory.GooglealsointroducednativecheckoutinsideAIsearchads,allowingpurchasesthroughGooglePaywithout

leavingthepage.ThecompanyalsoplanstoexpandthesefeaturestoYouTubewith

shoppableadssupportingembedded“BuyNow”flowspoweredbyUCPandGooglePay.

NewAISearchfocusedadformats:

ConversationalDiscoveryads:ConversationalDiscoveryadsappearwithinAI-generatedSearchresponsesinAIMode,directlyembeddedinthe

conversationalanswerflow,makingtheadfeellikepartoftheanswerratherthanaseparateplacement.TheyincludeanAI-generatedexplainerthat

synthesizesrelevantproductorserviceinformationalongsidetheadvertiser’smessaging,helpingprovidecontextandguidedecision-making,makingtheadfeellikepartoftheanswerratherthanaseparateplacement.

Exhibit2:ConversationalDiscoveryadsmaketheadfeellikepartoftheAIanswerGoogleConversationalDiscoveryads

Source:GoogleWebsite

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AI-poweredShoppingads:Theseadssurfacerelevantproductsand

automaticallygenerateshortexplanationsonwhyaproductfitsauser’sneeds.

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Theyaredesignedtosimplifydecision-making,especiallyforhigher-

considerationpurchases,withintheSearchexperienceitself.

DirectOffers:TheseareAI-drivenpromotionsthatsurfacepersonalizeddeals,suchasdiscountsorbundles,directlywithinSearch.Theyalsointegrate

featureslikenativecheckouttoreducefrictionandhelpuserscompletepurchasesmoreseamlessly.

BusinessAgentforLeads:ThisformatembedsanAI-poweredchatagent

withinanad,allowinguserstoaskquestionsandinteractdirectlywithabrand.

Itreplacesstaticleadformswithamoredynamic,conversationalwaytodrive

engagementandcaptureintent.

Exhibit3:BusinessAgentforLeadsembedsanAI-poweredchatagentwithinanad,allowinguserstoaskquestionsandinteractdirectlywithabrandBusinessAgentforLeads

Source:GoogleWebsite

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UniversalCartforpurchases/bookingsonGoogle

GooglelaunchedUniversalCart,apersistentcross-merchantshoppingcartthatenablesuserstosave,track,andmanageproductsacrossparticipatingretailersandGoogle

surfaces.Thecartwillallowuserstomakepurchaseswithinthecartforparticipating

retailers(includingTargetandWalmart,butnotAmazonsofar)orsendusersdirectlytoretailsitestocheckout(whichcouldbeapotentialoptionforAmazon).Googlealso

hintedthatfooddeliveryandhotelbookingcapabilitieswereonthehorizon.

UniversalCartworksasthefront-endlayerofGoogle’sagenticcommercestack.As

usersadditemsacrossGooglesurfaces,UCPactsastheorchestrationlayer,enabling

seamlessinteractionbetweenGoogleandmultiplemerchantsbystandardizingproductdata,supportingreal-timeinventoryandpricingchecks,andfacilitatingaunifiedcart

andcheckoutexperienceacrossretailers.Whenauserproceedstopurchase,AP2servesasthepaymentslayer,allowingAIagentstoexecutetransactionssecurelyontheuser’sbehalfbyverifyingauthorization,intent,andaccountability.Together,UCPpowersthecommerceworkflow,AP2ensurestrustedpaymentexecution,andUniversalCart

integratesbothintoasingle,continuousshoppingexperience.

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Exhibit4:Google’sUniversalCartenablesanagenticcheckoutexperienceacrossparticipatingretailersandGooglesurfacesGoogleUniversalCart

Source:

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PersistentSearchAgents

GoogleintroducedSearchAgents,persistentbackgroundAIagentsthatcontinuously

monitorinformationacrosscategoriesincludingfinance,shopping,travel,andsportsonbehalfofusers.Forshoppingrelatedusecases,theseagentscancontinuouslymonitorthemarketontheuser’sbehalf.Theytrackproductavailability,pricemovements,andnewproductlaunches,andsurfacerelevantpurchaseopportunitiestailoredtouser-

definedpreferenceswithoutrequiringrepeatedsearches.Operatinginthebackground,theysynthesizeinformationacrosssourcesandproactivelydeliverupdates,eliminatingtheneedformanualcomparisonandmonitoring.Beyonddiscovery,searchagentscanalsoinitiateandexecuteactions,includingverifyinginventory,facilitatingcheckout,orcoordinatingrelatedservicessuchasbookings.

MonetizationinAgenticEra

Wethinkpotentialmonetizationmodelsinagenticecosystemscouldinclude:

Productplacement:Agentsmayonlyshowafewrecommendedoptions.Brandswilllikelypaytobeincludedasanoption(sponsoredprompts)ortoappearhigherinthe

rankingifmultipleoptionsareshown.Adauctiondynamicswillshifttohigherbidsforlimited,high-intentslots.Forexample:Whenauseraskstheagent:“What’sthebest

laptopforworkunder$1,500?”PCbrandswilllikelypayahighpremiumtobeincludedasoneofasmallgroupofrecommendedproductsorhaveasidebaradthatlists

dynamicproductfeaturesthatarebestsuitedforthequery.Advertiserswillbid(CPC)tobeincludedasanoption,butwillnotpayarevenuesharewhenaagentcompletesatransaction.

Sponsoredactions(Outcome-basedmodel):Ahybridmodelcombiningelementsofadvertisingandcommissions,whereabrandappearsasasponsoredoptionbutonly

payswhentheagentactuallyexecutesthetask.Inthisstructure,pricingistiedtothe

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completionoftheaction,ratherthansimplytovisibilityorrecommendationplacement.

Insteadofpayingsolelytoappearinalistofrecommendations,brandsbidforthe“actionslot”.Paymentoccursonlyiftheactioniscompleted,aligningincentives

betweentheplatformandthebrandbylinkingmonetizationdirectlytosuccessfuloutcomes.Forexample,auseraskstheagent:“Bookmea4-starhotelinMiamiforunder$300.”Theagentevaluatesavailablehotelsandpresentsasmallnumberofoptions,suchas:

BookHyattMiamiBeach(Sponsored)

BookMarriottMiamiDowntown(Organic)

BookHiltonMiamiBeach(Organic)

Inthismodel,brandsthatmeetminimumqualification(saytop-5)couldbidtobecomeapreferredbookingoption.Iftheuserselectsthesponsoredoption,thereservationis

completedbytheagent,andtheadvertiserpaysonlyifthetransactionisexecuted.Thisstructureblendsadvertisingwithperformance-basedmonetization,effectivelyturningtheagentintoatransactionaldecisionlayer,ratherthanjustadiscoveryinterface.

Revenuesharearrangements(Commissionbased):AIplatformspresentunbiasedresultsandtakesapercentageoftransactionscompletedbytheagent.Forexample:Ifuserasks“MexicanrestaurantfordinnerinthefinancialdistrictonWednesday”,the

agentprovidesalistofoptionswhichdoesnotincludeanysponsoredlistings,andthencompletesthereservationbasedontheuser’schoice.On