Multi-agentAI–21stcenturyautomationrevolution

Automationhasbeenthefundamentaltechnology

underpinningeconomictransformationforcenturies.

WhetheritwasBritain’slate18thand19thcenturyindustrialrevolution,theUnitedStates’post-WorldWarIIboom,orSouthKorea’sindustrializationstartinginthe1960s,allbenefitedfromautomationtoincreaseproductivity,efficiency,andprofitability.Theeffectwasradicaltransformationofeconomyandsociety.1Now,inthe21stcentury,anewwaveofautomationisunderwayasagenticAIusewidensacrosseconomies.

AIagentsemployarangeofadvancedtechnologiestointeractwithusersandperformtasksautonomouslyandeffectively.Largelanguagemodels(LLMs)areoftentheprimaryinterfacebetweenAIagentsandusers.Theyareatypeoffoundationmodeltrainedonvastdatasets,primarilytext.LLMsencodeknowledgebyrecognizingpatterns,enablingAIagentstoreason,informtheirdecision-making,andcommunicate.

Anagentcanunderstandandgeneratehuman-liketextorverbalresponsesusingnaturallanguageprocessing(NLP),makinghuman-AIinteractionsmorenaturalandefficient.

AnaΓtificialintelligenceagentisasoftwareprogramthatcaninteractwithitsenvironment,collectdata,andusethistoautonomouslyperformtaskstomeetpredeterminedgoals.Asanevolutionfromtechnologieslikeroboticprocessautomation(RPA)andmachinelearning(ML),AIagentscan,perceive,reason,and

actinchangingenvironmentstoachievetheirgoals.Howtheyreachthemislargely

lefttothemtodecide.

1D.AcemogluandP.Restrepo,JournalofEconomicPerspectives,Vol.33,

AutomationandNewTasks:HowTechnology

DisplacesandReinstatesLabor,

pp.3–30

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Multi-agentAI–21stcenturyautomationrevolution

AgenticAIdoingwhatitdoesbest

SincegenerativeAItookoffasapopularphenomenon,companieshaverushedtocreatetheirownversions.Theyareusedforadvancedsearch,analysisandinteractionwithdocuments,particularlyinthelegal,HR,andtechnologyfields.Whiletheseimproveonprevioussystems,tostopatthispointunderestimatesthefullpotential.LLMsareimprovingiteratively–wheretwoyearsagoanLLMwithretrieval-augmentedgeneration(RAG)couldproducesynopses–currentLLMsusingenhancedretrievalmethodscangeneratemoresophisticatedoutput.

ThefundamentaldifferencebetweenusingastandaloneLLMandemployingamulti-agentsystemisthatwiththelatter,individualagentsarecreatedtospecializeinspecifictasks–oftennotlimitedtolanguage–andcancollaboratewitheachother.Theycanexecutemorecomplextasksandintegratewithexternaltoolssuchaswebsearches,APIs,anddedicateddatabases.

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Multi-agentAI–21stcenturyautomationrevolution

Tangibleandintangiblevalue

WhyshouldenterprisesgotheagenticAIroute?Inshort,becauseitwillsoonbeineverybusinessfunctionwhereit’sfeasible.

82%

oforganizations

plantointegrateAIagentsby2027.2

TherearemorereasonstomovetoagenticAIthancost-savingalone.

AccordingtoaUKCustomerSatisfactionsurvey,adefiningfeatureofhigh-performingorganizationsis“anappropriatebalanceofpeopleandtechnology,combiningspeed,efficiency,andpersonalcare.”

Inotherwords,theidealretailcustomerserviceexperienceisablendofhumanempathyandtechnologicalefficiency.3BusinessescanthereforeuseagenticAItoimproveanddifferentiatetheiroffertocustomersaheadofcompetitors,e.g.,byaddingcommunicationchannelsandstylesthatappealtospecificcustomerbases.

Companiesarere-organizingtheircustomerandITsupportservicestoraisequalitystandardsbyaugmentingthemwithAIagentsinhybridmodels.Forexample,anAIagentcanautomaticallydraftresponsestocustomerqueriesbasedonhistoricalcustomerinteractiondata.

Moreambitiously,agentscantakeownershipofaclientissue.Previously,ifacustomercontactedachatbottorequestarefundforaproductandtherequestwasnon-standard,thechatbotwouldescalatetheissuetoahumancustomer-servicerepresentative.Now,anAIagentcanrequestmoreinformationfromacustomer,forexample,proofofpurchaseoraphotographofanitem,analyzetheenquiryandofferapossiblesolution.Itcandecidealonetooverridestandardprocedure,ifcircumstancesjustifymakinganexception.Itishighlylikelytoresolvethematterwithouthumaninput.

2CapgeminiResearchInstitute,

HarnessingthevalueofgenerativeAI:

2ndedition

,2024

3UKIndex(UKCSI),

January2025

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Multi-agentAI–21stcenturyautomationrevolution

Designfor

maximumΓetuΓn

AnAIagenticsystemincreasesoverallproductivity,servicequality,customersatisfaction,andloyalty.Thedesignofagent-to-agentandagent-to-humanexchangesaccordingtoframeworks,rules,risks,andprotocolsiscrucialforthis.

Unlikepredecessors,AIagentsare:

?Autonomous

?Goal-oriented

?Context-aware,usingrelevantdatatomakedecisions

?Adaptive,adjustingbehaviorandresponsesasdataorinteractionschange

?Proactive,initiatingactionindependentlywithoutuserprompts

?Language-aware,interpretingandrespondinginhumanlanguage

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Multi-agentAI–21stcenturyautomationrevolution

Age∩tcreation

TobuildAIagentsrequires:

1.Definingtheirroles,

2.Identifyingandlocatingthedatatheywilluse,3.Definingwhichtasksorgoalstheyexecute,

4.Settingboundarieswithguardrails.

Withmultipleagents,eachhasitsownspecializedrole,whileitcooperateswithothersinadecentralizedstructure.Theycansolvemorecomplextaskscollaboratively,suchasprocessinginsuranceclaims:oneagentverifiesdocumentation,anotherevaluatespolicycriteria,andathirdprocessespayments,completingthetaskjointly.

Typicalgoalsforagents:

Customerservice

Content

recommendation

Predictive

analytics

Fraud

detection

Real-timemonitoring

Schedulingand

taskmanagement

Inventory

management

Orchestrationinmanufacturing

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Multi-agentAI–21stcenturyautomationrevolution

Theagenticsystem

Thevisualrepresentationofanagenticprocesscancloselyresembletraditionalorganizationalprocessdiagrams.Asorganizationstransitiontowardagenticsystems,theirbusinessexpertsshouldcollaboratecloselywithAIspecialiststoeffectivelydesignandstreamlinetheseprocesses.However,integratingAIagentsintoexistingsystemscanbecomplex,disruptive,anddestabilizingtooperationsifnotmanagedcarefully.

Buildinganarchitecturethataccuratelyreflectsreal-worldactivitiesfirstrequirescreatingdigitaldescriptionsanddefinitionsofbusinessoperations.ClearlydefinedtaskscanthenbemappedtoAIagentsasneeded.Thisapproachcontrastswithtraditionalmethods,whichimposeapredefinedarchitectureontoabusinessandarchetypesontoitspeople.

Inanagent-firstenterprise,modelingagenticinteractioninvolvesvisualizingcommunicationandtransactionsbetweeninternalandexternalparties.Thisincludesunderstandingtheunderlyingmotivation,similartotraditionalbusinessprocessmodeling.Organizationsshouldmapallsuchinteractionstoaccuratelyrepresenttheentirevaluenetwork,notonlythevaluechain.

DataasAIoxygen

Datarequiresoptimizationasthefoundationfortheentireagenticarchitecture,sinceitsfragmentationblocksAIagentsfromworkingeffectively.80%oforganizationsstoremorethan50%oftheirdatainhybridandmulti-cloudinfrastructures,4whichcomplicatesintegration,availability,andmanagement.ThiscanhaveanimpactonAIagentdeploymentandeffectiveness.

Tooptimizedataforagenticusemeans:

?Evaluatingitsquality

?Establishinggovernance,management,andsecurity

?Creatingapipelinetoensurereal-timeornear-real-timeavailabilityofhigh-qualitydata

?Continuousmonitoring

?Ongoingimprovementusingfeedbackloops

4Tremblay,T.,Kohezion,

Whatisdatafragmentation?

,September2,2024

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Multi-agentAI–21stcenturyautomationrevolution

EvolutiontowardsagenticAI

Acrosssectorsandindustries,whicheverdomaindecidestoimplementagenticAIarchitecture,asimilarprocessappliesacrossspecializationstomaximizereturnsoninvestment.

LeapingintoagenticAIimmediatelywithoutfirstevaluatingsimplerautomationoptionsisakintocreatingasolutioninsearchofaproblem:RPAandscriptsalonecanachieveautomationfortasksthathavedefinedstableinputsandtreatment.AgenticAIshouldbeadoptedspecificallyforachievinghyper-automation–advanced,next-generationautomationfortasksthathavevariableinputsandvariabletypesoftreatment.

1.Maptheprocesses–conversiontoagenticAIrequirespreparationforautomation.Processesarebrokendowninsub-processes,macrotasks,andmicrotasks.

Whentheframeworkisappliedtoafinancialandaccountingdigitaloperationsuchasacredit-to-cashprocess,itcanbebrokendowninto:

12macrotasks

57microtasks

6sub-processes

Level1

CredittoCash

6sub-processes

Assesscredit

Resolvedisputes

Collectcash

Reconcile

Applycash

Level2

Createknowledge

Level3

12macrotasks

Level4

57microtasks

%ofautomation/augmentation

GenAI

AgenticAI

RPA/script

Humans

x

x

x

xx

xx

xxxxx

To-be

As-is

Figure1Exampleofaccountingcredit-to-cashprocess,withdifferentlevelsofautomations(RPA/script,AgenticAI,GenAI)

2.ConvertexistingarchitectureintoautomationcomponentsstartingwithRPA/scriptsfortaskswithdefinedandstableinput,thenhyperautomationwithAgenticAIfortasksthathavevariablesinputsandvariabletypesoftreatment

3.Finally,fortasksthathavenotbeenautomated,augmenthumanworkerswithAIassistantspoweredbyGenerativeAI.

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Multi-agentAI–21stcenturyautomationrevolution

Governance,guardrails,andresponsibility

Asmentionedearlier,itisamulti-agentsystem’scapabilityforspecializedroles,distributeddecision-making,autonomouscoordinationandcollaborationthatdistinguishesitfromanon-agenticGenAIsystem.Itsgreatercomplexitynecessitatesspecificgovernancestrategies.

Inamulti-agentsystem,agentsaremonitoredwhile“trusted”toactindependently,withselectiveuseofhumanoversight.AIagentsrequireclearlydefinedgovernanceframeworksthatspecifywhenandinwhatformhumanauthorizationisnecessary,forexample:

?Duringinitialconfiguration

?Atcriticaldecisionpointsinaworkflow

?Beforeinteractingwithsensitivedata

?Inoperationsinvolvingotheragentsandhumans

HumaninterventionisasafeguardincaseAIagents’decision-makingisbiased,inaccurate,orbreachescompanyethics.Such“misbehavior”coulddamageclient

andemployeetrust,negativelyimpactbrandreputation,orresultinlegalviolations.

Testingforcomplianceandfailure,includingforbias,fairness,andoperationalperformance,isessential.Theaimistoestablishpointsoffailureanddefineboundariesforguardrails.Theresultsshoulddemonstrateagents’compliancewithregulationsandbethebasisforcontingencyplansincaseoffailure.

TheautonomyofAIagentsmakesitmorecomplextotraceerrorsanddeterminetheirrootcauses.Whenthereisafault,itisnotabreakageinclassicITform.Issuesarehardertoreplicate,giventhecomplexity,sophisticationanduniquenessofeachinstanceofaction.Agentactivitymustbesystematicallylogged,capturingperformedtasks,actionstaken,evaluationmetrics,andtheagent’sinternalstateforeffectivemonitoringanderrortracing.Thesearedefinedandtestedintheproof-of-conceptstageandupdatedasneededduringtheagent’swholelifecycle.

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Multi-agentAI–21stcenturyautomationrevolution

Orchestrationandintegration

Processeswithpotentiallythousandsoflargelyautonomousdigitalworkerswillneedcarefulorchestration.Successdependsonmaximizingautomationwithinawell-designedarchitecture.Withoutit,thereisriskofbreakdownordisruption.

ThepredictedwideuseofagenticAIwillpushITspecialiststowardsdevelopingskillstosupportanagenticAIsystembyonboarding,managing,andtrainingAIagentsas“digitalworkers”.

ManaginganagenticAIsystemrequires:

?Configuringagentsforspecificroles,similartorecruitingandonboardinghumanemployees

?Checkingthatagentscomplywithrelevantlaws,includingtheEU’sGDPRandAIAct

?Designinghuman-agentcollaboration

?LeadingchangemanagementwithemployeetrainingonAIadoption

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Multi-agentAI–21stcenturyautomationrevolution

Thesectorsandindustriesalreadyastepahead

ThearrivalofagenticAIineconomiesaroundtheworldwillpromptorganizationstoreviewtheirprocessesforsuitabilityandpotentialgainsinproductivityandcostsaving.ThefollowingsectorsandindustriesarealreadyonanagenticAIjourney,butmorewilljoinasexpertiseinadoptionspreads.

Consumer

AI-poweredinteractivehome

assistantdevicesusedtooversee

theelderlyandinfirm,locatemislaiditems,andmonitorhomesecurity

LifeSciences

?Drugdiscoverysupporttoextractactionableinsightsfromdrug

mechanisms,diseaseprogressionandclinicaloutcomes

?Refiningclinicaltrialdesignand

monitoringreal-timedataformid-trialadjustments

RetailandSupplyChain

AI-drivenagentsmonitoring

shelvesin-storeandwarehousesandautomaticallytriggerst