麥肯錫-為什么代理是生成式人工智能的下一個(gè)前沿 Why agents are the next frontier of generative AI 2024

July2024

Mcsey

&company

QuantumBlack

AIbyMckinsey

McKinseyDigitalPractice

WhyagentsarethenextfrontierofgenerativeAI

Bymovingfrominformationtoaction—thinkvirtualcoworkers

abletocompletecomplexworkflows—thetechnologypromisesanewwaveofproductivityandinnovation.

ByLareinaYee,MichaelChui,andRogerRobertswithStephenXu

Overthepastcoupleofyears,theworldhas

marveledatthecapabilitiesandpossibilities

unleashedbygenerativeAI(genAI).Foundation

modelssuchaslargelanguagemodels(LLMs)canperformimpressivefeats,extractinginsightsandgeneratingcontentacrossnumerousmediums,

suchastext,audio,images,andvideo.ButthenextstageofgenAIislikelytobemoretransformative.

Wearebeginninganevolutionfromknowledge-based,genAI–powered

tools—say,chatbotsthatanswerquestions

andgeneratecontent—togenAI–enabled“agents”thatusefoundationmodelstoexecutecomplex,

multistepworkflowsacrossadigitalworld.Inshort,thetechnologyismovingfromthoughttoaction.

Broadlyspeaking,“agentic”systemsreferto

digitalsystemsthatcanindependentlyinteractinadynamicworld.Whileversionsofthese

softwaresystemshaveexistedforyears,the

natural-languagecapabilitiesof

genAIunveilnew

possibilities

,enablingsystemsthatcanplantheiractions,useonlinetoolstocompletethosetasks,

collaboratewithotheragentsandpeople,andlearntoimprovetheirperformance.GenAIagents

eventuallycouldactasskilledvirtualcoworkers,

workingwithhumansinaseamlessandnatural

manner.Avirtualassistant,forexample,couldplanandbookacomplexpersonalizedtravelitinerary,

handlinglogisticsacrossmultipletravelplatforms.

Usingeverydaylanguage,anengineercould

describeanewsoftwarefeaturetoaprogrammeragent,whichwouldthencode,test,iterate,and

deploythetoolithelpedcreate.

Agenticsystemstraditionallyhavebeendifficulttoimplement,requiringlaborious,rule-based

programmingorhighlyspecifictrainingof

machine-learningmodels.GenAIchangesthat.

Whenagenticsystemsarebuiltusingfoundation

models(whichhavebeentrainedonextremelylargeandvariedunstructureddatasets)ratherthan

predefinedrules,theyhavethepotentialtoadapt

todifferentscenariosinthesamewaythat

LLMs

canrespondintelligibly

topromptsonwhichtheyhavenotbeenexplicitlytrained.Furthermore,usingnaturallanguageratherthanprogrammingcode,

ahumanusercoulddirectagenAI–enabled

agentsystemtoaccomplishacomplexworkflow.Amultiagentsystemcouldtheninterpretand

organizethisworkflowintoactionabletasks,assign

worktospecializedagents,executetheserefinedtasksusingadigitalecosystemoftools,and

collaboratewithotheragentsandhumanstoiterativelyimprovethequalityofitsactions.

Inthisarticle,weexploretheopportunitiesthat

theuseofgenAIagentspresents.Althoughthe

technologyremainsinitsnascentphaseand

requiresfurthertechnicaldevelopmentbefore

it’sreadyforbusinessdeployment,it’squickly

attractingattention.Inthepastyearalone,Google,Microsoft,OpenAI,andothershaveinvestedin

softwarelibrariesandframeworkstosupport

agenticfunctionality.LLM-poweredapplications

suchasMicrosoftCopilot,AmazonQ,andGoogle’supcomingProjectAstraareshiftingfrombeing

knowledge-basedtobecomingmoreaction-based.

CompaniesandresearchlabssuchasAdept,

crewAI,andImbuealsoaredevelopingagent-basedmodelsandmultiagentsystems.Giventhespeed

withwhichgenAIisdeveloping,agentscould

becomeascommonplaceaschatbotsaretoday.

Whatvaluecanagentsbringtobusinesses?

Thevaluethatagentscanunlockcomesfromtheirpotentialtoautomatealongtailofcomplexuse

casescharacterizedbyhighlyvariableinputsand

outputs—usecasesthathavehistoricallybeen

difficulttoaddressinacost-ortime-efficient

manner.Somethingassimpleasabusinesstrip,forexample,caninvolvenumerouspossibleitinerariesencompassingdifferentairlinesandflights,not

tomentionhotelrewardsprograms,restaurant

reservations,andoff-hoursactivities,allofwhich

mustbehandledacrossdifferentonlineplatforms.Whiletherehavebeeneffortstoautomatepartsofthisprocess,muchofitstillmustbedonemanually.

Thisisinlargepartbecausethewidevariationin

potentialinputsandoutputsmakestheprocesstoocomplicated,costly,ortime-intensivetoautomate.

GenAI–enabledagentscaneasetheautomationof

complexandopen-endedusecasesinthreeimportantways:

WhyagentsarethenextfrontierofgenerativeAI2

—Agentscanmanagemultiplicity.Manybusinessusecasesandprocessesarecharacterizedbyalinearworkflow,withaclearbeginningand

seriesofstepsthatleadtoaspecificresolutionoroutcome.Thisrelativesimplicitymakesthemeasilycodifiedandautomatedinrule-based

systems.Butrule-basedsystemsoftenexhibit“brittleness”—thatis,theybreakdownwhen

facedwithsituationsnotcontemplatedbythe

designersoftheexplicitrules.Manyworkflows,forexample,arefarlesspredictable,markedbyunexpectedtwistsandturnsandarangeof

possibleoutcomes;theseworkflowsrequire

specialhandlingandnuancedjudgmentthat

makesrules-basedautomationchallenging.

ButgenAIagentsystems,becausetheyare

basedonfoundationmodels,havethepotentialtohandleawidevarietyofless-likelysituationsforagivenusecase,adaptinginrealtimeto

performthespecializedtasksrequiredtobringaprocesstocompletion.

—Agentsystemscanbedirectedwithnatural

language.Currently,toautomateausecase,itfirstmustbebrokendownintoaseriesofrules

andstepsthatcanbecodified.Thesestepsaretypicallytranslatedintocomputercodeand

integratedintosoftwaresystems—anoften

costlyandlaboriousprocessthatrequires

significanttechnicalexpertise.Becauseagenticsystemsusenaturallanguageasaformof

instruction,evencomplexworkflowscan

beencodedmorequicklyandeasily.What’s

more,theprocesscanpotentiallybedonebynontechnicalemployees,ratherthansoftwareengineers.Thismakesiteasiertointegrate

subjectmatterexpertise,grantswideraccesstogenAIandAItools,andeasescollaborationbetweentechnicalandnontechnicalteams.

—Agentscanworkwithexistingsoftwaretoolsandplatforms.Inadditiontoanalyzingand

generatingknowledge,agentsystemscanusetoolsandcommunicateacrossabroaderdigitalecosystem.Forinstance,anagentcanbe

directedtoworkwithsoftwareapplications

(suchasplottingandchartingtools),search

thewebforinformation,collectandcompile

humanfeedback,andevenleverageadditionalfoundationmodels.Digital-tooluseisbotha

definingcharacteristicofagents(it’soneway

thattheycanactintheworld)butalsoawayin

whichtheirgenAIcapabilitiescanuniquelybe

broughttobear.Foundationmodelscanlearn

howtointerfacewithtools,whetherthrough

naturallanguageorotherinterfaces.Without

foundationmodels,thesecapabilitieswould

requireextensivemanualeffortstointegrate

systems(forexample,usingextract,transform,andloadtools)ortediousmanualeffortsto

collateoutputsfromdifferentsoftwaresystems.

HowgenAI–enabledagentscouldwork

Agentscansupporthigh-complexityusecases

acrossindustriesandbusinessfunctions,

particularlyforworkflowsinvolvingtime-consumingtasksorrequiringvariousspecializedtypesof

qualitativeandquantitativeanalysis.Agentsdothisbyrecursivelybreakingdowncomplexworkflows

andperformingsubtasksacrossspecialized

instructionsanddatasourcestoreachthedesiredgoal.Theprocessgenerallyfollowsthesefour

steps(Exhibit1):

1.Userprovidesinstruction:AuserinteractswiththeAIsystembygivinganatural-language

prompt,muchlikeonewouldinstructatrustedemployee.Thesystemidentifiestheintendedusecase,askingtheuserforadditional

clarificationwhenrequired.

2.Agentsystemplans,allocates,andexecutes

work:Theagentsystemprocessesthepromptintoaworkflow,breakingitdownintotasksandsubtasks,whichamanagersubagentassignstootherspecializedsubagents.Thesesubagents,equippedwithnecessarydomainknowledge

andtools,drawonprior“experiences”and

codifieddomainexpertise,coordinatingwitheachotherandusingorganizationaldataandsystemstoexecutetheseassignments.

3.Agentsystemiterativelyimprovesoutput:

Throughouttheprocess,theagentmayrequestadditionaluserinputtoensureaccuracyand

relevance.Theprocessmayconcludewiththe

agentprovidingfinaloutputtotheuser,iteratingonanyfeedbacksharedbytheuser.

WhyagentsarethenextfrontierofgenerativeAI3

Exhibit1

AgentsenabledbygenerativeAIsooncouldfunctionashypere代cientvirtualcoworkers.

Agentsystem

Illustrationofhowan

agentsystemmight

Manageragent

executeaworklow,

Externalsystems:

Agentsinteractwith

databasesandsystems—

bothorganizationaland

externaldata—to

completethetask.

fromprompttooutput

Analystagent

Planneragent

Checkeragent

Specialistagents

3

1

2

End

Start

4

Usingnaturallanguage,theuserpromptsthe

generativeAIagentsystemtocompleteatask.

Theagentsysteminterpretsthepromptandbuildsa

workplan.Amanageragentsubdividestheprojectintotasksassignedtospecialistagents;theygatherand

analyzedatafrommultiplesourcesandcollaborate

withoneanothertoexecutetheirindividualmissions.

Theagentteamsharesthedraftoutputwith

theuser.

Theagentteamreceivesuserfeedback,theniteratesand

re?nesoutputaccordingly.

McKinsey&Company

4.Agentexecutesaction:Theagentexecutesanynecessaryactionsintheworldtofullycompletetheuser-requestedtask.

Artofthepossible:Threepotentialusecases

Whatdothesekindsofsystemsmeanfor

businesses?Thefollowingthreehypothetical

usecasesofferaglimpseofwhatcouldbepossibleinthenot-too-distantfuture.

Usecase1:Loanunderwriting

Financialinstitutionspreparecredit-riskmemosto

assesstherisksofextendingcreditoraloantoa

borrower.Theprocessinvolvescompiling,analyzing,andreviewingvariousformsofinformationpertainingtotheborrower,loantype,andotherfactors.Given

themultiplicityofcredit-riskscenariosandanalyses

required,thistendstobeatime-consumingand

highlycollaborativeeffort,requiringarelationship

managertoworkwiththeborrower,stakeholders,

andcreditanalyststoconductspecializedanalyses,whicharethensubmittedtoacreditmanagerfor

reviewandadditionalexpertise.

Potentialagent-basedsolution:Anagentic

system—comprisingmultipleagents,eachassumingaspecialized,task-basedrole—couldpotentially

bedesignedtohandleawiderangeofcredit-risk

scenarios.Ahumanuserwouldinitiatetheprocess

byusingnaturallanguagetoprovideahigh-level

workplanoftaskswithspecificrules,standards,andconditions.Thenthisteamofagentswouldbreak

downtheworkintoexecutablesubtasks.

Oneagent,forexample,couldactasthe

relationshipmanagertohandlecommunications

WhyagentsarethenextfrontierofgenerativeAI4

betweentheborrowerandfinancialinstitutions.

Anexecutoragentcouldcompilethenecessary

documentsandforwardthemtoafinancialanalystagentthatwould,say,examinedebtfromcashflowstatementsandcalculaterelevantfinancialratios,whichwouldthenbereviewedbyacriticagentto

identifydiscrepanciesanderrorsandprovidefeedback.Thisprocessofbreakdown,analysis,refinement,andreviewwouldberepeateduntilthefinalcreditmemoiscompleted(Exhibit2).

UnlikesimplergenAIarchitectures,agentscan

producehigh-qualitycontent,reducingreviewcycletimesby20to60percent.Agentsarealsoableto

traversemultiplesystemsandmakesenseofdatapulledfrommultiplesources.Finally,agentscan

showtheirwork:creditanalystscanquicklydrill

intoanygeneratedtextornumbers,accessingthecompletechainoftasksandusingdatasourcestoproducethegeneratedinsights.Thisfacilitatestherapidverificationofoutputs.

Usecase2:Codedocumentationandmodernization

Legacysoftwareapplicationsandsystemsat

largeenterprisesoftenposesecurityrisksand

canslowthepaceofbusinessinnovation.But

modernizingthesesystems

canbecomplex,costly,andtime-intensive,requiringengineerstoreview

andunderstandmillionsoflinesoftheolder

codebaseandmanualdocumentationofbusinesslogic,andthentranslatingthislogictoanupdatedcodebaseandintegratingitwithothersystems.

Potentialagent-basedsolution:AIagentshave

thepotentialtosignificantlystreamlinethisprocess.Aspecializedagentcouldbedeployedasa

legacy-softwareexpert,analyzingoldcodeand

documentingandtranslatingvariouscodesegments.Concurrently,aqualityassuranceagentcould

critiquethisdocumentationandproducetestcases,helpingtheAIsystemtoiterativelyrefineitsoutputandensureitsaccuracyandadherenceto

Exhibit2

GenerativeAIagentshavethepotentialtochangethewayweworkbysuperchargingproductivity.

Illustrativeusecase:credit-riskmemos

Financialinstitutionsoftenspend1–4

weekscreatinga

credit-riskmemo.

Thecurrentprocess:

Start

TheRManda

creditanalyst

collaboratively

analyzethedata.

Thecreditanalysttypicallyspends20+hourswritingthememo.

Arelationshipmanager(RM)

gathersdatafrom

15+sourceson

borrower,loantype,andotherfactors.

TheRMreviewsthememoandprovidesfeedback;

thecreditanalystwritesanewdraftincorporatingthefeedback.

3

2

1

End

4

Start

2

1

3

End

GenerativeAI

(genAI)agentscouldcuttimespenton

creatingcredit-riskmemosby20–60%usingthesesteps:

TheRMpromptsthegenAIagentsystemand

providesrelevantmaterialsneeded

toproducethememo.

Theagentsubdivides

theprojectintotasks

thatareassignedto

specialistagents,whichgatherandanalyzedatafrommultiplesources

andthencollaboratetogenerateadraftmemo.

TheRMandcredit

analystreviewthe

memoandgive

feedback;theagentincorporatesthe

feedbackintothe?nalmemo.

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WhyagentsarethenextfrontierofgenerativeAI5

organizationalstandards.Therepeatablenatureof

thisprocess,meanwhile,couldproduceaflywheel

effect,inwhichcomponentsoftheagentframeworkarereusedforothersoftwaremigrationsacrosstheorganization,significantlyimprovingproductivityandreducingtheoverallcostinsoftwaredevelopment.

Usecase3:OnlinemarketingcampaigncreationDesigning,launching,andrunninganonline

marketingcampaign

tendstoinvolveanarrayofdifferentsoftwaretools,applications,and

platforms.Andtheworkflowforanonline

marketingcampaignishighlycomplex.Business

objectivesandmarkettrendsmustbetranslated

intocreativecampaignideas.Writtenandvisual

materialmustbecreatedandcustomizedfor

differentsegmentsandgeographies.Campaignsmustbetestedwithusergroupsacrossvarious

platforms.Toaccomplishthesetasks,marketing

teamsoftenusedifferentformsofsoftwareand

mustmoveoutputsfromonetooltoanother,whichisoftentediousandtime-consuming.

Potentialagent-basedsolution:Agentscanhelpconnectthisdigitalmarketingecosystem.For

example,amarketercoulddescribetargeted

users,initialideas,intendedchannels,andotherparametersinnaturallanguage.Then,anagentsystem—withassistancefrommarketing

professionals—wouldhelpdevelop,test,anditerate

differentcampaignideas.Adigitalmarketing

strategyagentcouldtaponlinesurveys,analyticsfromcustomerrelationshipmanagementsolutions,andothermarketresearchplatformsaimedat

gatheringinsightstocraftstrategiesusing

multimodalfoundationmodels.Agentsforcontentmarketing,copywriting,anddesigncouldthenbuildtailoredcontent,whichahumanevaluatorwould

reviewforbrandalignment.Theseagentswould

collaboratetoiterateandrefineoutputsandaligntowardanapproachthatoptimizesthecampaign’simpactwhileminimizingbrandrisk.

Howshouldbusinessleaderspreparefortheageofagents?

Althoughagenttechnologyisquitenascent,

increasinginvestmentsinthesetoolscouldresultinagenticsystemsachievingnotablemilestonesandbeingdeployedatscaleoverthenextfewyears.As

such,itisnottoosoonforbusinessleaderstolearnmoreaboutagentsandconsiderwhethersomeof

theircoreprocessesorbusinessimperativescanbeacceleratedwithagenticsystemsandcapabilities.Thisunderstandingcaninformfutureroadmap

planningorscenariosandhelpleadersstayattheedgeofinnovationreadiness.Oncethosepotentialusecaseshavebeenidentified,organizationscanbeginexploringthegrowingagentlandscape,

utilizingAPIs,toolkits,andlibraries(forexample,

MicrosoftAutogen,HuggingFace,andLangChain)tostartunderstandingwhatisrelevant.

Topreparefortheadventofagenticsystems,

organizationsshouldconsiderthesethreefactors,whichwillbekeyifsuchsystemsaretodeliverontheirpotential:

—Codificationofrelevantknowledge:

Implementingcomplexusecaseswilllikely

requireorganizationstodefineanddocument

businessprocessesintocodifiedworkflowsthatarethenusedtotrainagents.Likewise,

organizationsmightconsiderhowtheycan

capturesubjectmatterexpertise,whichwillbeusedtoinstructagentsinnaturallanguage,thusstreamliningcomplexprocesses.

—Strategictechplanning:Organizationswill

needtoorganizetheirdataandITsystemsto

ensurethatagentsystemscaninterface

effectivelywithexistinginfrastructure.That

includescapturinguserinteractionsfor

continuousfeedbackandcreatingtheflexibilitytointegratefuturetechnologieswithout

disruptingexistingoperations.

—Human-in-the-loopcontrolmechanisms:As

genAIagentsbegininteractingwiththerealworld,controlmechanismsareessentialtobalance

autonomyandrisk(seesidebar,“Understandingtheuniquerisksposedbyagenticsystems”).

Humansmustvalidateoutputsforaccuracy,

compliance,andfairness;workwithsubject

matterexpertstomaintainandscaleagent

systems;andcreatealearningflywheelfor

ongoingimprovement.Organizationsshouldstartconsideringunderwhatconditionsandhow

suchhuman-in-the-loopmechanismsshouldbedeployed.

WhyagentsarethenextfrontierofgenerativeAI6

Understandingtheuniquerisksposedbyagenticsystems

Largelanguagemodels(LLMs),aswenowknow,arepronetomistakesand

hallucinations.BecauseagentsystemsprocesssequencesofLLM-derived

outputs,ahallucinationwithinoneoftheseoutputscouldhavecascadingeffectsifprotectionsarenotinplace.

Additionally,becauseagentsystems

aredesignedtooperatewithautonomy,

businessleadersmustconsideradditionaloversightmechanismsandguardrails.

Whileitisdifficulttofullyanticipatealltherisksthatwillbeintroducedwithagents,herearesomethatshouldbeconsidered.

Potentiallyharmfuloutputs

Largelanguagemodelsarenotalways

accurate,sometimesprovidingincorrectinformationorperformingactionswith

undesirableconsequences.Theserisks

areheightenedasgenerativeAI(genAI)agentsindependentlycarryouttasks

usingdigitaltoolsanddatainhighly

variablescenarios.Forinstance,anagent

mightapproveahigh-riskloan,leadingtofinancialloss,oritmaymakeanexpensive,nonrefundablepurchaseforacustomer.

Mitigationstrategy:Organizations

shouldimplementrobustaccountabilitymeasures,clearlydefiningthe

responsibilitiesofbothagentsand

humanswhileensuringthatagentoutputscanbeexplainedandunderstood.This

couldbeaccomplishedbydeveloping

frameworkstomanageagentautonomy(forexample,limitingagentactions

basedonusecasecomplexity)and

ensuringhumanoversight(forexample,

verifyingagentoutputsbeforeexecutionandconductingregularauditsofagent

decisions).Additionally,transparencyandtraceabilitymechanismscanhelpusers

understandtheagent’sdecisionmakingprocesstoidentifypotentiallyfraught

issuesearly.

Misuseoftools

Withtheirabilitytoaccesstoolsanddata,agentscouldbedangerousifintentionallymisused.Agents,forexample,couldbe

usedtodevelopvulnerablecode,createconvincingphishingscams,orhack

sensitiveinformation.

Mitigationstrategy:Forpotentially

high-riskscenarios,organizationsshouldbuildinguardrails(forexample,access

controls,limitsonagentactions)and

createclosedenvironmentsforagents(forinstance,limittheagent’saccesstocertaintoolsanddatasources).

Additionally,organizationsshouldapplyreal-timemonitoringofagentactivities

withautomatedalertsforsuspicious

behavior.Regularauditsandcompliancecheckscanensurethatguardrailsremaineffectiveandrelevant.

Insufficientorexcessivehuman–agenttrust

Justasinrelationshipswithhuman

coworkers,interactionsbetweenhumansandAIagentsarebasedontrust.Ifuserslackfaithinagenticsystems,theymightscalebackthehuman–agentinteractionsandinformationsharingthatagentic

systemsrequireiftheyaretolearnandimprove.Conversely,asagentsbecomemoreadeptatemulatinghumanlike

behavior,someuserscouldplacetoo

muchtrustinthem,ascribingtothem

human-levelunderstandingandjudgment.

Thiscanleadtousersuncritically

acceptingrecommendationsorgivingagentstoomuchautonomywithout

sufficientoversight.

Mitigationstrategy:Organizationscanmanagetheseissuesbyprioritizing

thetransparencyofagentdecision

making,ensuringthatusersaretrainedintheresponsibleuseofagents,and

establishingahumans-in-the-loop

processtomanageagentbehavior.Humanoversightofagentprocesses

iskeytoensuringthatusersmaintainabalancedperspective,critically

evaluateagentperformance,andretainfinalauthorityandaccountabilityin

agentactions.Furthermore,agent

performanceshouldbeevaluatedbytying

agents’activitiestoconcreteoutcomes(forexample,customersatisfaction,

successfulcompletionratesoftickets).

Inadditiontoaddressingthesepotentialrisks,organizationsshouldconsiderthebroaderissuesraisedbygenAIagents:

—Valuealignment:Becauseagentsareakintocoworkers,theiractionsshouldembodyorganizationalvalues.What

valuesshouldagentsembodyintheirdecisions?Howcanagentsbe

regularlyevaluatedandtrainedtoalignwiththosevalues?

—Workforceshifts:Bycompletingtasksindependently,agentsystemsstand

tosignificantlyalterthewayworkis

accomplished,potentiallyallowing

humanstofocusmoreonhigher-leveltasksthatrequirecriticalthinkingandmanagerialskills.Howwillrolesand

responsibilitiesshiftineachbusinessfunction?Howcanemployeesbe

providedwithretraining

opportunities?Aretherenew

collaborationmodelsthatcanenhancecooperationbetweenhumansandAIagents?

—Anthr