2025 AI智能体的实践应用：评估与治理基础框架白皮书（英文）

IncollaborationwithCapgemini

AIAgentsinAction:

FoundationsforEvaluationandGovernance

WHITEPAPER

NOVEMBER2025

Images:AdobeStock

Contents

Foreword4

Executivesummary

5

Introduction

6

1EvolvingtechnicalfoundationsofAIagents8

1.1ThesoftwarearchitectureofanAIagent

8

1.2Communicationprotocolsandinteroperability

10

1.3Cybersecurityconsiderations

12

2FoundationsforAIagentevaluationandgovernance13

2

.1Classification

14

2

.2Evaluation

19

2

.3Riskassessment

22

2

.4GovernanceconsiderationsforAIagents:aprogressiveapproach

25

3Lookingahead:multi-agentecosystems

29

Conclusion30

Contributors31

Endnotes34

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©2025WorldEconomicForum.Allrightsreserved.Nopartofthispublicationmaybereproducedortransmittedinanyformorbyanymeans,includingphotocopyingandrecording,orbyanyinformation

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AIAgentsinAction:FoundationsforEvaluationandGovernance2

AIAgentsinAction:FoundationsforEvaluationandGovernance3

November2025

AIAgentsinAction:FoundationsforEvaluationandGovernance

Foreword

RoshanGya

ChiefExecutiveOfficer,CapgeminiInvent

Inrecentyears,organizationshavemovedbeyondpredictivemodelsandchatinterfacestoexperimentwithartificialintelligence(AI)inmoretransformativeways.AIagentsarenowemergingasintegrated

collaboratorsinbusiness,publicservicesand

everydaylife.TheadoptionofAIagentscould

bringsignificantgainsinefficiency,alteredkindsofhuman-machineinteractionandtheadventofnoveldigitalecosystems.

Thistransitionfacesmultipleobstaclesthatneedtobeaddressed.Movingfrommodelstoagentsrepresentsmorethanatechnicalmilestoneandrequires

organizationstorethinkhowtheydesign,evaluateandgovernadvancedagenticsystems.Manycompaniesarenowquestioningwhatagentscanaccomplish

alongsidethepracticalstepsneededtoadoptanddeploythemsafely,responsiblyandeffectively.

Thispaperwasdevelopedtohelpanswerthose

questions.Bymappingtheevolvingfoundations

ofagenticsystems,classifyingtheirroles,

identifyingnewwaystoevaluatethemandoutliningprogressivegovernanceapproaches,thepaper

offerspracticalguidanceforleadersnavigatingadoptioninreal-worldcontexts.

CathyLi

Head,CentreforAI

Excellence,Memberof

theExecutiveCommittee,WorldEconomicForum

ThroughtheAIGovernanceAlliance,theWorld

EconomicForumandCapgeminiareadvancing

thissubjectincollaborationwiththeAIcommunity,signallingthatnowisthetimetoprepareforan

agenticfuture.Ifadoptersstartsmall,iterate

carefullyandapplyproportionatesafeguards,

agentscanbedeployedinwaysthatamplify

humancapabilities,unlockproductivityand

establishafoundationformorecomplexmulti-agentecosystemstoemergeovertime.Unlessacarefulanddeliberateapproachtoadoptionisadopted,

untestedusecasescouldoutpaceoversightandleadtomisalignedincentives,emergentrisksandlossofpublictrust.

Aswithanytransformativetechnology,the

opportunitiespresentedbyAIagentsmustbe

accompaniedbyaresponsibilitytoguidetheir

developmentanddeploymentwithcare.Through

cross-functionaleffortsandcollaborative

governance,AIagentscanbeintegratedinways

thatamplifyhumaningenuity,promoteinnovation

andimproveoverallqualityoflife.Thispaperisa

stepinthatdirection,offeringguidancetohelpearlyadoptersnavigatethecomplexandoftenuneven

pathofAIagentadoption.

AIAgentsinAction:FoundationsforEvaluationandGovernance4

Executivesummary

ThispaperexplorestheemergenceofAI

agents,outliningtheirtechnicalfoundations,classification,evaluationandgovernancetosupportsafeandeffectiveadoption.

ThisreporthasbeentailoredmainlyforadoptersofAIagents,includingdecision-makers,technicalleadersandpractitionersseekingtointegrateAI

agentsintoorganizationalworkflowsandservices.

WhileAIagentsaregainingtraction,thereremainslimitedguidanceonhowtodesign,testand

overseethemresponsibly.Thispaperaimstohelpfillthatgapbyprovidingastructuredfoundationforthesafeandeffectivedeploymentofthesesystems.

Thepapermakesthreekeycontributions.Firstly,itcoversthetechnicalfoundationsofAIagents,

includingtheirarchitectures,protocolsandsecurityconsiderations.Secondly,itoffersafunctional

classificationthatdifferentiatesagentsbytheirrole,autonomy,authority,predictabilityandoperational

context.Thirdly,itsuggestsaprogressivegovernanceapproachthatdirectlyconnectsevaluationand

safeguardstoanagent’staskscopeanddeploymentenvironment.

Together,theseelementsguideadopterswitha

conceptualblueprintformovingfromexperimentationtodeployment.ThereporthighlightstheimportanceofaligningadoptionwithevaluationandgovernancepracticestoensurethatAIagentsaresuccessfullydeployedwhiletrust,safetyandaccountability

aremaintained.

AIAgentsinAction:FoundationsforEvaluationandGovernance5

Introduction

AIagentsareshiftingfromprototypesto

deployment,bringingbothtransformative

opportunitiesandnovelgovernancechallenges.

AIagentsaregraduallybecomingembeddedinanincreasingnumberoftasks,workflowsand

usecasesthatspancloudandedgecomputing,leadingthewaytomorewidespreadadoption.

Asthetransitionfromprototypingtodeployment

accelerates,currentadoptionremainsconcentratedamongearlyadopters.Accordingtoarecentglobalsurveyofexecutives,82%oforganizationsplantointegrateagentswithinthenextonetothreeyears,indicatingthatmosteffortsarestillintheplanningorpilotphase,1whilemovingtowardswideradoption.

Theconceptofsoftwareagentshasbeenstudiedfordecadesinfieldssuchasrobotics,autonomoussystemsanddistributedcomputing.Whatisdifferent

todayistheriseofdata-drivenmodels,particularlygenerativeartificialintelligence(AI)andlargelanguagemodels(LLMs),whichareenablingtheemergenceofanewgenerationofLLM-basedagents.Thesesystemscangenerateplans,simulatereasoningandadapttheirbehaviourthroughfeedback

mechanismsinwaysthatwerepreviouslynot

possible.Thisevolutionhassparkedanew

waveofexperimentation,withresearchersandcompaniesrapidlycreatingprototypesofagentsinvariousfields.Thisreportfocusesmainlyon

LLM-basedagents(“AIagents”issometimes

usedinshort),whosegrowingcapabilitiescreatebothsignificantopportunitiesforadoptionandanewsetofchallengesingovernanceandsafety.

1

FIGURE

FoundationsfortheresponsibleadoptionofAIagents

1

Technicalfoundations

Laythe

groundwork

2

Functionalclassification

3

Evaluationandgovernance

Definetheagent’srole

Scalewithconfidence

AIAgentsinAction:FoundationsforEvaluationandGovernance6

LLM-basedAIagents,forexample,introducenewriskssuchasgoalmisalignment,behaviouraldrift,

toolmisuseandemergentcoordinationfailures

thattraditionalsoftwaregovernancemodelsare

unabletomanage.Unlikeconventionalsoftware,

agentsareincreasinglyassumingrolesthat

resemblethoseofhumandecision-makersrather

thanstatictools.Thismeansthatgovernance

modelsdesignedsolelyforaccesscontroland

systemreliabilityarenolongersufficient.Amore

usefulcomparisonisthegovernanceapplied

tohumanusers,whomustearnpermissions,

accountabilityandtrustbydemonstratingperformanceovertime.Similarly,trustinAIagentscanbe

establishedbytestingtheirbehaviouragainst

validatedcases,runningtheminhuman-in-the-loopconfigurationsandgraduallyexpanding

autonomyonlyoncereliabilityhasbeensufficientlydemonstrated.Inbothcases,theprincipleofleastprivilegeremainsessential,withaccesslimitedtoinformationandactionsnecessaryforthetask.

Thisreportaimstoprovideaforward-lookinganalysisoftheevolvinglandscapeofAIagents,focusing

onthecapabilities,infrastructure,classificationandsafeguardsnecessaryforresponsibledeployment.

Tothisend,itisstructuredaroundfourfoundational

pillarsacrossclassification,evaluation,riskassessmentandgovernance,whichtogetherformthefoundationforaprogressiveapproachtoadoptionand

deployment.Figure1presentsthegeneralcontentofthisreport,whichhelpsguidetheresponsibleadoptionanddeploymentofAIagents.

Thegoalistoequipadopters,providers,technicalleaders,organizationaldecision-makersandotherstakeholderswithasharedunderstandingofthecurrentstateofagenticsystemsandemerging

oversightpractices.Buildingonestablished

AIgovernanceprinciplesandframeworks,

suchasthosedevelopedbytheOrganisation

forEconomicCo-operationandDevelopment

(OECD),2NationalInstituteofStandardsand

Technology(NIST),3InternationalOrganizationforStandardization(ISO)/InternationalElectrotechnicalCommission(IEC)4andothers,thispaper

introducesadditionalprinciplesaddressing

autonomy,authority,operationalcontextand

systemicriskthatextendexistinggovernance

guidancefromanagent-focusedlens.The

insightshavebeeninformedbyworkinggroup

meetings,workshopsandextensiveinterviewswithmembersoftheSafeSystemsandTechnologies

workinggroupoftheAIGovernanceAlliance.

AIAgentsinAction:FoundationsforEvaluationandGovernance7

Evolvingtechnical

foundationsofAIagents

Thearchitecture,protocolsandsecurity

modelsofAIagentsdictatehowtheyintegrateintoorganizationsandinteractwiththeworld.

WhilethecorearchitectureofAIagentsisbeginningtotakeshape,practicesforagentdeployment,

integrationandgovernanceremainnascent.As

organizationsbeginto“hire”AIagentstosupportoraugmenthumanteams,orperformtasksthatimpactthephysicalworld,adoptionshouldbetreated

withthesamelevelofrigourasonboardinganew

employee,includingclearlydefinedroles,safeguardsandstructuredoversightmechanisms.Thissectionoutlinesthetechnicalfoundationsthatenableagenticsystemsandthearchitecturedecisionsthatshape

howtheyarebuilt,deployedandgoverned.

1

ThesoftwarearchitectureofanAIagent

Buildingagentsrequiresnotjustengineeringbut

alsoorchestrationandcoordinationbetweenmodels,tools,datasourcesandhumans.

1.1

TheadoptionofLLM-basedagentsbyindustry

marksabroadershiftinsoftwaredevelopmentfromrigid,rules-basedsystemstomoreflexible,intent-driveninteractions.Forinstance,incallcentres,

earlychatbotsthatfollowedscripteddecisiontreesarenowgivingwaytoagenticsystemscapable

ofunderstandingintent,managingcontext,and

escalatingdecisionsmoredynamically.ThisevolutiontowardsagenticAIrepresentsafundamentalchangeincontrolandautonomy,wheretaskstraditionallyperformedbyhumansaredelegatedtomachines.

Toenablethisshift,AIagentsdrawonfourtechnologicalparadigms:

–Classicalsoftware:deterministiclogicandrule-basedexecution

–Neuralnetworks:patternrecognitionandstatisticallearning

–Foundationmodels:general-purpose,adaptivesystemsthatinterpretinstructionsandact

contextually

–Autonomouscontrol:mechanismsthatenablesystemstoplan,coordinateandactwithminimalhumanoversight

Asaresult,buildingagentsrequiresnotjust

engineeringbutalsoorchestrationandcoordination

betweenmodels,tools,datasourcesandhumans.Thislayeredsetupintroducesnewcomplexity

inhowagentsbehave,generalizeandinteractwiththeirenvironment,reinforcingtheneedforstructuredscaffolding.

Today,AIagentarchitecturesareorganizedinto

threeinterconnectedlayers,consisting

ofapplication,orchestrationandreasoning,

whichcollectivelyenableintelligent,context-

awareandbusiness-alignedautomation.Atahighlevel,agentarchitecturesaredesignedtointerfacewithusersandsystems,coordinatecomplextasksusingexternaltoolsandapplicationprogramminginterfaces(APIs),andsupportdecision-making

throughacombinationoflanguagemodels,

reasoningmodulesandcontrollogic.Together,theselayersprovidethetechnicalfoundationthatunderpinshowagentsoperate.

Theapplicationlayer,alongwithprotocolssuchasModelContextProtocol(MCP)andagent-to-

agentprotocol(A2A),integratestheagentinto

specificprocessesoruserworkflows.ItreceivesinputthroughuserinterfacesorAPIsandtranslatesitintostructuredsignals.Applicationlogicappliesdomain-specificrulesandconstraintstoensuretheagent’soutput(i.e.forecast,decisions,actions,messages,etc.)isalignedwithuserexpectationsandbusinessrequirements.Thislayercanruninthecloudoron-preminedgecomputingequipment.

Understandingthisarchitectureiskeytoanticipatinghowagentswill

engagewithusersandsystems,

coordinate

workflowsandmakecontext-awaredecisions.

FIGURE2

Theorchestrationlayer(frameworklayer)

governshowtheagentinterpretsinputs,invokestoolsandcoordinatestasks.WhilesomeLLM

providers5haveintegratedtoolsdirectlyintotheirsolutions,thiscancreaterigidandvendor-lockedsystems.Agenticframeworksovercomethis

bystandardizingtoolsandsystemsintegration,remainingLLM-agnosticandspanningmultipleworkloadsacrosscloudandedge.ThisenablesAIagentstoemployarangeofreasoningstrategiesandsupportfeatures,suchascodeexecutionorsearch,anduseprotocolslikeMCPtoconnectwithenterpriseresources,includingdatabasesandcustomerrelationshipmanagement(CRM)systems.Mostagentsalsoincludespecializedsub-agentsthathandledistincttasks,whichmakesthemfunctionallypartofamulti-agentsystem.

Theorchestrationlayeriscriticalinthisregard,asitcoordinatessub-agents,assignsresponsibilitiesandmanagesdependenciesbetweenthem.Italsoenablesmodelswitching,allowingorganizationstoassigndifferentmodelstovarioustasksbasedontheircomplexity,costorperformance.Importantly,AIagentshaveauniquearchitecturethatcan

beextendedbeyondtheorganization’ssecurity

perimeter.Theirabilitytoinvokeexternaltoolsandcommunicatewithotheragentsenablesthemto

SoftwarearchitectureofanAIagent

operatebeyondtraditionalnetworkboundaries,introducingnovelcybersecurityconcerns.

Thereasoninglayerunderpinstheagent’sabilitytogenerate,predict,classifyorapplyrulesin

pursuitofitsgoals.Dependingonthetask,the

reasoninglayercandrawonarangeofmodels,

includingdeterministic,rule-basedapproaches

andclassicalmachinelearning,aswellassmall

orlargelanguagemodelsandothergenerative

architectures.Thechoiceofmodelshapeshow

theagentprocessesinformation,adaptstocontextandultimatelycarriesoutitsassignedrole.

Figure2illustratesthislayeredarchitecture,showinghowinternalcomponentsacrossapplication,

orchestrationandreasoningworktogetherto

supportdynamicagentbehaviourwhilemaintainingsecureboundariesacrossorganizationalsystems.

Incombination,theselayersconstitutethe

technicalbackbonethatgovernsagent

functionality.FororganizationsimplementingAIagents,understandingthisarchitectureiskeytoanticipatinghowagentswillengage

withusersandsystems,coordinateworkflowsandmakecontext-awaredecisions.

EventUser

Application

Orchestration

Reasoning

Environment

Alagentboundary

ITapplications

Percepts

Actions

CRM

Messaging

Database

Input/output

UI

API

Code

Agenticframework

PlanningMemoryToolsWorkflow

GenerativeNon-generativeMechanistic

Reasoning

Application

Orchestration

Models

Alagent

MCP

Alagent

A2A

AIAgentsinAction:FoundationsforEvaluationandGovernance8

InternalorganizationresourcesThird-partyresources

Communicationprotocolsandinteroperability

1.2

MCPhasgainedwidespreadsupportacrossleading

agentframeworksandisincreasinglyviewedasacoremechanism.

ThelandscapeofadvancedLLM-basedagents

issupportedbynewprotocolsthatenablemore

seamlessintegrationandcollaboration.TheMCP,

forexample,aimstostandardizetheconnection

betweenenterprisesoftwaresystems,external

datasourcesandagents,whileprotocolssuch

asA2Aandthe

AGNTCY

architecture’sagent

connectprotocol(ACP)offertoolstofacilitate

interactionbetweenvaryingAIagents,formingthe

interoperabilitylayerformulti-agentsystems(MAS).Astheseprotocolsareimplementedacrosscloud

platforms,enterprisenetworksandedgedevices,

theyarenecessaryforrunningagenticcodewhile

connectingwithreal-worldsensordataandsystems.

IntroducedbyAnthropicinlate2024,MCP6enablesagentstoconnectwithinternalor

externaldatasources,APIsandenterprisesystemsthroughastandardizedprotocol.

Ratherthandevelopingbespokeintegrations

foreachagent-taskpairing,MCPallowsagentstoactasclientsthatrequestaccesstoservicesviaMCP-compliantservers.Forexample,an

agentusingMCPcancheckacalendar,retrieveemails,updatedatabasecontentorupdate

CRMrecordsthroughasharedinterface.

Thissignificantlyreducesfriction,speedsup

deploymentandsupportsmodularplug-and-playcapabilitiesacrosstoolsandenvironments.

MCPhasgainedwidespreadsupportacrossleadingagentframeworksandisincreasinglyviewedasacoremechanismforconnecting

agentstothebroaderenterpriseinfrastructure.

IllustrationofMCP-basedagentcommunication

FIGURE3

MCP

client

messaging

MCP

client

database

MCP

client

database

AIagent1

AIagent2

1

Userupdatesarecord

Sendanemail

Readarecord

MCP

server

messaging

MCP

server

database

4

Acknowledgeupdate

3

2

MCPserverupdatesthedatabase

Messaging

Databaseupdateconfirmed

Database

AIAgentsinAction:FoundationsforEvaluationandGovernance9

OverviewofMCP

AIAgentsinAction:FoundationsforEvaluationandGovernance10

WhereMCPfocusesoncommunicationbetweenagentsandexternalorinternalsystems,protocolslikeA2Aenableagentstodiscovereachother,

interact,collaborateanddelegatetasks,whetheroperatingwithinanorganization’ssecurityperimeteroroutsideit.Theseprotocolsaddressagrowingneedincomplexenvironmentswheremultiple

agentsworktogetheracrossorganizationalortechnicalboundaries,enablingagentsfromdifferentvendorstocommunicateeffectively.

ReleasedbyGoogleinApril2025,7A2Aoperatesthroughacommoncommunicationinterfaceandintroducestheconceptofagentcards(similartomodelcards8),whicharestructureddescriptionsofanagent’sidentity,alongwithitscapabilities

andskills.Thisallowsforautomaticdiscoveryandcoordinationbetweenagentsandsystems.

4

FIGURE

Illustrationofagent-to-agentcommunicationprotocol

A2Aprotocol

Agentscard

Task

manager

MCP

A2A

Artefacthandler

APIsandenterpriseapplications

APIsandenterpriseapplications

AIagent2

Agents

LLM

Agentframework

AIagent1

Agentframework

Agents

LLM

Beyondcommunicationanddiscovery,new

standardsarealsoemergingthataddresshowagentstransactandexchangevalue.ReleasedbyGoogleinSeptember2025,theAgent

PaymentsProtocol(AP2)9enablessecure,auditabletransactionsunderuser-definedlimits.UnlikeMCPandA2A,whichfocusondataexchangeandtaskcoordination,AP2addressescomplexfinancialoperations.

Despitethisprogress,interoperabilityremainsakeychallenge.Technicalcompatibilityalonedoesnotguaranteesuccessfulcoordination

betweenagents.Strategy,privacyandsecurityconsiderationsoftenshapehowandwhether

systemsshouldbeintegratedandareimportantforenterprisestocarefullyconsider.

Forexample,communicationbetweendifferentagentscouldraiseconcernsaboutaccesscontrol,dataconfidentialityorcomplianceacrossjurisdictions.Choosingwhethertoexposeacapabilitytootheragentsbecomesagovernancedecisionasmuchasatechnicalchoice.

AIAgentsinAction:FoundationsforEvaluationandGovernance11

Cybersecurityconsiderations

Security

strategieshave

evolvedfrom

perimeterdefencestolayered“defenceindepth”andmorerecentlytothe

zero-trustmodel.

1.3

AsAIagentsmoveintoenterpriseandconsumer-facingenvironments,theyextendratherthan

replaceexistingsecuritychallenges.Security

strategieshaveevolvedfromperimeterdefencestolayered“defenceindepth,”andmorerecentlytothezero-trustmodel.10Thesechangesreflectbroadertransformationssuchascloudadoption,distributedworkforcesandinterconnected

ecosystems,allofwhichhavealreadyweakenedthenotionofaclearboundarybetweeninternalandexternalnetworks.Agentsbuildonthis

trajectorybutaddadditionallayersofriskthatmustbemanagedproactively.

Byautonomouslyinvokingtoolsandcommunicatingacrossorganizationallines(e.g.viaMCPand

A2A),agentsembedexternalservices,databasesandpeeragentsintoenterpriseworkflows.Thismultiplicationofidentitiesandconnectionsmakesidentitymanagement,micro-segmentationandongoingverificationofagentactivityessential.

WhileprotocolssuchasMCPandA2Acan

streamlineintegration,theyalsoexpandtheattacksurface11byintroducingnewexternaldependenciesandinterfaces,asillustratedinFigure2.Thevery

interoperabilitythatenhancesagentcapabilities

alsoexposesenterprisestounpredictableinputsandvulnerabilitiesfromthirdparties.Foradopters,thismeansthateveryagentinteractionshouldbetreatedasuntrustedbydefault,andthatverifyingidentity,permissionsandcontextisnecessary

beforegrantingaccess.

Finally,agentscanbemisused.12Theymightbe

exploitedthroughdesignflawsorpromptinjections,orevenintentionallydeployedformaliciouspurposes,suchasaccessingprivatedataorspreading

misinformation.Unliketraditionalattacks,autonomousagentscanactwithspeedandpersistence,makingattributionandaccountabilityharder.Organizationsshouldprepareforthisbyimplementingstrong

audittrails,incidentresponseplansandclearaccountabilitystructures.

AIAgentsinAction:FoundationsforEvaluationandGovernance12

2

Systematic

classificationis

importantbecauseitprovidesa

commonbasisforcomparingagents,anticipatingrisksandlinking

evaluationandgovernance.

FoundationsforAIagentevaluationandgovernance

AstructuredfoundationforevaluatingandgoverningAIagentsenablesconsistent

assessmentandoversightacrosscontexts.

AsAIagentsmatureandadoptionincreases,a

functionalunderstandingoftheirrolesandpropertiesisbeginningtotakeshape.Ratherthanclassifying

agentssolelybymodality(e.g.text,speech,vision)

ordomain(e.g.customerservice,decisionsupport,workfloworchestration),itismoreeffectiveto

evaluatethemaccordingtotheirintendedpurpose,corepropertiesandoperatingcontext.Thisapproachcreatesaclearerfoundationforassessingimpacts

anddesigningsafeguardsthatareproportionatetoanagent’srole.Systematicclassificationisimportantbecauseitprovidesacommonbasisforcomparingagents,anticipatingrisksandlinkingevaluation

andgovernancedecisionstotherealitiesofhow

anagentoperates.Withoutit,oversightrisksmaybecomeinconsistent,reactiveordisconnectedfromanagent’sactualcapabilitiesandenvironment.

Toestablishthisfoundation,thisreport

introducesfourfoundationalpillarswhich,incombination,provideastructured

approachtoassessmentandadoption:

–Classification:Establishtheagent’scharacteristicsandoperationalcontexttoinformdownstreamassessment.

–Evaluation:Generateevidenceofperformanceandlimitationsinrepresentativesettings.

–Riskassessment:Analysepotentialharmusingclassificationandevaluationasinputs.

–Governance:Translateclassification,evaluationandriskassessmentresultsintosafeguardsandaccountability

proportionatetotheagent’sprofile.

ThesefoundationsapplytodiverseAIagents,encompassingbothvirtualandembodied

systemsindifferentoperationalcontexts.

Theyprovideaconsistentbasisforassessing

performance,identifyingrisksandestablishinggovernancemechanismsthatscalewithan

agent’sautonomy,authorityandfunction.

Toaddressclassification,evaluation,riskassessmentandgovernance,itisusefultodistinguishbetweentwomainstakeholderperspectives:13

–Provider:ReferstoorganizationsorindividualsthatsupplyAIsystems,platformsortools.Theirresponsibilitiesincludeensuringthatproductsaredevelopedandmaintainedinaccordance

withresponsibleandethicalguidelines,and

thatthenecessarydocumentationandsupportareprovided.

–Adopter:Referstoindividualswithinan

organizationwhouseAIsystems,encompassingresponsibilitiessuchasprocurementand

deployment.Procurementinvolvesthe

responsibilityofacquiringAIsolutionsfor

organizationalusebyconductingduediligenceandensuringthatallAIagentsolutionscomply

withorganizationalpoliciesandregulatory

requirements.DeploymentistheresponsibilityforimplementingAIsystemsinaccordancewithdocumentedrequirementsandplans,while

ensuringthatrisksandimpactsoftheAIagentareproperlyassessedandmanaged.

Theadopterdependsontheproviderfor

transparentdocumentation,modelandsystem

specifications,andsufficientperformanceandriskinformationtosupportresponsibledeploymentandoversightthroughoutthesystemlifecycle.

Thefourpillarsformacontinuousandparallel

progressioninwhichclassificationprovides

structure,evaluationestablishesevidence