2026 AI 智能体深度指南

TheInnerCircleGuidetoAgenticAI

Researchsupportedby

7AIAgentUseCasesforDigital-FirstEnterprises

Customerexpectationsarerisingfasterthanheadcount.Toimproveproductivityandscaleoperations,enterprisesareadoptingAIagentsthatcanunderstandnaturallanguage,integratewithbackendsystemsandcompletemeaningfulwork.

1.High-volumetransactionalrequests5.Claimpreventionandassistedtroubleshooting

High-volumetransactionalinteractionsincludetracking

packages,confirmingorderstatus,requestinginvoice

copiesandchangingdeliverydates.Theserequestsfollowpredictableworkflowsandrelyonstructuredsystemdata.

AIagentscanretrievedata,executeupdatesandconfirmcompletion,helpingreducewaittime,loweraveragehandletimeandprovide24/7availability.

Manyclaimsstemfromproductmisuse,minorissuesor

misunderstoodsetupinstructions.AIagentscanguide

customersthroughtroubleshooting,clarifyinstructionsand

evaluatefactorssuchaswarrantyeligibilityandcomplianceconditionsduringtheinteraction.Thiscanreduceunnecessaryclaims,processingcostsandreverselogisticsexpenses.

2.Real-timeissueresolutioninthemoment

Customersoftencallatmomentsofurgency,suchas

authenticationerrors,failedpickuplockersordeliveryissues.AIagentscandetecturgency,validateidentity,retrieveaccountdataandinitiatecorrectiveactionswithoutdelay.This

supportsimmediateresolution,reducesrepeatcontactsandhelpsmaintaintrust.

3.Intelligentroutingandagentspecialization

Someinteractionsrequirehumanexpertise,includingbilling

disputes,warrantyescalationsandcomplexconfiguration

requests.Beforetransfer,AIagentsclassifyintent,gather

contextandrouteinteractionstotheappropriatespecializedteam.Humanagentsreceivepre-qualifiedconversations

ratherthanrawinboundcalls,improvingfirstcontactresolutionandproductivity.

4.Mid-conversationtopicswitching

Customersrarelyfollowlinearpaths.Arefunddiscussionmaybecomeawarrantyclaim,oradeliveryenquirymayshifttoproductconfiguration.ModernAIagentsdetecttopicshifts,pauseoneworkflow,loadtherelevantprocessandpreservecontext,reducingrestartedcallsandimprovingcompletionrates.

6.Peakdemandandtrafficrouting

Holidayspikes,productlaunchesandservicedisruptionscancreatetrafficsurges.AIagentsscaleinstantly,createdynamictickets,prioritizeurgentcasesanddefernon-criticalissues.

Thishelpsmaintainstableservicelevelswithoutseasonalhiring.

7.Autonomousqualitymanagement

Traditionalqualityassuranceteamsmanuallysample

afractionofcalls.Modernplatformsevaluate100%of

interactionsautomatically,analyzingperformance,complianceadherenceandconversationalquality.Thisenablesobjectivemeasurement,eliminatessamplingbiasandcontinuous

improvementacrosscustomerconversations.

Learnhowintelligence,augmentation,andautomationcanworktogethertodeliverbettercustomerexperiences

3

TheInnerCircleGuidetoAgenticAI(USedition)PublishedJuly2026

©ContactBabel2026

Pleasenotethatallinformationisbelievedcorrectatthetimeofpublication,but

ContactBabeldoesnotacceptresponsibilityforanyactionarisingfromerrorsoromissionswithinthereport,linkstoexternalwebsitesorotherthird-partycontent.

4

CONTENTS

Contents 4

TableofFigures 5

WhatisAgenticAI? 7

EvolutionofAIintheContactCenter 7

WhyareUSorganizationsimplementingAI(ofanykind)? 10

AgenticAIandagent-washing 11

HowAgenticAIWorks 11

AgenticAICharacteristics 14

AgenticAIDeploymentModels 15

Openstandardsandinteroperability 16

UseCasesforAgenticAI 17

AI-EnabledLiveAgentAssistance 17

AI-EnabledSelf-Service&DigitalChannelSupport 22

AgenticAIforSelf-Service 22

AgenticAIforDigitalChannels 24

AI-EnabledAnalytics 27

ImplementingAgenticAI 29

ImplementationSteps 29

CommercialandPricingModels 32

GovernanceFrameworksforAgenticAI 33

RegulationandAgenticAI(NorthAmerica) 34

SecuritywhenAIcanact 36

KeystoSuccessandPitfallstoAvoid 36

TheFutureofAgenticAIintheContactCenter 38

AboutContactBabel 40

5

TABLEOFFIGURES

Figure1:Theevolutionfromrule-basedautomationtoagenticAI 8

Figure2:MostimportantoutcomesfromcurrentorfutureuseofAI 10

Figure3:TheagenticAIoperatingloop 12

Figure4:Aspectsofagentic,conversationalandgenerativeAI 13

Figure5:CommonagenticAIdeploymentmodels 15

Figure6:HowMCPandA2Afittogether 16

Figure7:Historicalmeancallduration(service&sales),2012-2025 17

Figure8:Historicalaveragespeedtoanswer&callabandonmentrate,2012-2025 18

Figure9:Whycustomersmovefromwebself-servicetolivetelephony 22

Figure10:Levelofautomationusedinwebchat,2019-25 24

Figure11:Levelofautomationusedinemailmanagement(2019-2025) 25

Figure12:Modelsofhumanoversight,frommosttoleastinvolved 31

Figure13:ExampleagenticAIgovernanceroles 34

CallMineristhegloballeaderincustomerexperience(CX)automation,poweredbydeepconversationintelligence.

LeveragingAIagentsandhumanexpertise,CallMinerdeliverssmarter,moreefficientinteractionsthattransformCXandreduceoperationalcosts.

AdvancedAIandindustry-leadinganalyticsturneveryconversationintoactionableintelligence,drivingprocessoptimization,performancegains,customer

engagement,andenterprise-wideautomation.

CallMineristrustedtoimproveCXforleadingbrandsacrosstechnology,mediaandtelecom,retail,manufacturing,financialservices,healthcare,andtravel&hospitality.

Tolearnmore,visit

CallM

,readthe

CallMinerblog

,orfollowuson

LinkedIn

,

X

and

Facebook

.

7

WHATISAGENTICAI?

Theterm“agenticAI”isappearingeverywhere,soitisbesttostartthisstudyofhowitcanbeusedinthecontactcenterenvironmenttospecifywhatweactuallymeanbythis.

Atthetoplevel,“agenticAI”referstosystemscapableoftakingautonomousactionstowardachievingadefinedgoal.

Unliketraditionalbotsorstaticautomation,agenticAIbehaveslikeadigitalemployee:

planning,executingandadaptingtasksacrosssystemswithoutconstanthumaninstruction.TrueagenticAIexhibitsspecificcharacteristics:

•Autonomy:operateswithouthumanpromptingoncegoalsareset

•Goal-oriented:actstofulfilspecificobjectives(e.g.resolveabillingdispute)

•Initiative-driven:proactivelytakesstepsratherthanwaitingforinput

•Adaptability:learnsfromoutcomesandadjustsstrategiesaccordingly

•Tool-using:interfaceswithothersystemssuchasCRM,ticketingplatformsanddatabasesasnecessary.

EVOLUTIONOFAIINTHECONTACTCENTER

TheuseofAIinthecontactcenterhascomealongway,veryquickly.

Theterm“artificialintelligence”isusedverylooselybyindustrycommentators,solutionprovidersandorganizations,somuchsothatitnowseemstocoveralmosteveryuseoftechnologyinthecontactcenter.

Lookingattheuseof“AI”inthewidestsense,wecanseewhereagenticAIfitsin:

1:Rule-BasedAutomation

•Simplescriptsanddecisiontrees(IVRs,chatbotswithlimitedflows)

•Humanescalationrequiredforanythingoutsidepresetrules

•Considerableusageandsuccesswithhandingsimplewebchat

2:ConversationalAI

•Naturallanguageunderstanding(NLU)enablesmoredynamicdialogues

•Stillreactiveandlargelydependentonstructuredinteractions

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3:GenerativeAI

•LLMs(largelanguagemodels)generateresponses,summariesandcontent

•Greatlyimprovesknowledgeretrieval,butlackstaskautonomy

•Providesinformationratherthanaction

4:AgenticAI

•Combinesconversationalandgenerativecapabilities

•Addsautonomousdecision-making,actionexecutionandplanning

•Representstheshiftfromautomationtotrueautonomy.

Figure1:Theevolutionfromrule-basedautomationtoagenticAI

—callMiner

IntelligentAutomation

PoweringtheFutureofCX

CallMinercombinesAI-poweredconversationintelligencewithadvancedautomationandagentaugmentationtoimprovecustomerexperienceandreduceoperationalcosts.

Helpingyouembracethefutureofcustomerexperience

Intelligence

Captureandanalyze100%ofomnichannelinteractionswithspeedandaccuracy.Turnconversationsintoreal-time,AI-driveninsightsforsmarterdecisionsandbusinessimprovement.

Augmentation

Empoweragentswithcoachingandreal-timeguidance.

Augmentfrontlineteamstodeliverfasterresolutions,

improvecustomersatisfaction,anddelivermore

effective,empatheticCX.

Automation

FuelCXautomationtoreducecostsandaccelerateoutcomes.Engagecustomersdirectlythroughprocessautomation,

proactiveoutreach,andnatural,voice-optimizedvirtualagents.

Download7AIAgentUseCasesforDigital-FirstEnterprises

Howtouseintelligentautomationto

meetrisingcustomerexpectationswhilescalingoperationsandefficiency

10

WHYAREUSORGANIZATIONSIMPLEMENTINGAI(OFANYKIND)?

Conventionalwisdomsaysthatitistoreducethecostofpayingsalariesandwhilethereisalmostcertainlyanelementofthis,itdoesnotseemtobetheprimarymotive.

Figure2:MostimportantoutcomesfromcurrentorfutureuseofAI

MostimportantoutcomesfromcurrentorfutureuseofAI

4%

15%

25%

56%

39%

Cutcalldurations

20%

28%

13%

Supportmultiplelanguages

41%

15%

44%

Predictcustomerbehavior

26%

39%

19%

16%

16%

17%5%

10%1%

3%3%

Reduceagentheadcount

28%

28%

31%

25%

Detectfraudattempts

50%

Reducecallqueues

38%

50%

Improveknowledgebase

44%

50%

Handlemoreenquiriesthroughself-service

47%

52%

2%

Improveaccuracyofresponses

52%

33%

15%

Betterunderstandcustomers

0%10%20%30%40%50%60%70%80%90%100%

CriticallyimportantImportantOflimitedimportanceUnimportant

Infact,while19%ofUScontactcenterssaidthatreducingagentheadcountwasofcriticalimportancetothem,55%saidthatitwasofeitherlimited,ornoimportance.

AIislookedupontobevitalasameanstoincreasethesophistication,accuracyand

effectivenessofself-service,andalsoasawaytoimprovetheunderstandingofcustomers.

Theimprovedprovisionofinformation–quickerandmoreaccurate–acrossallchannelsisalsoakeydriverofAIimplementation.

Improvingtelephonyoperationalperformancethroughreducedcalllengthsandqueuetimesisseenaslessimportant,despitethepowerfuleffectthiscanhaveoncustomer

experience.

11

Itshouldbenotedthatalthoughheadcountreductionisoneoftheleastpopularofthe

optionsprovided,thelargemajorityofthereasonsforimplementingAIcouldactuallyleadtothis.Forexample,thebenefitsfromsuccessfullycuttingcalldurationcouldleadtoshorter

queuetimeswiththesamenumberofagentsemployed.However,itcouldalternativelyleadtoreducedheadcountifnoimprovementtoqueueperformanceKPIsweredesired.

AIproducesefficiencies,anditisthenuptotheorganizationtodecidewhattodowiththem.

AGENTICAIANDAGENT-WASHING

Thelooseuseoftheterm“artificialintelligence”notedabovehasacommercialedgetoit.AsagenticAIhasbecomethephrasethatbuyersrespondto,agrowingnumberofsolutions

havebeenrelabeledtomatch,andestablishedchatbots,scriptedworkflowsandroboticprocessautomationarenowroutinelypresentedasagents.Thispracticehasbecome

commonenoughtoearnitsownname:“agent-washing”.

Thedistinctionthatmatterstoabuyerisapracticalone.Agenuineagenticsystemsetsagoal,plansthestepsneededtoreachit,actsacrossthesystemsinvolvedandadaptsbasedontheresult,whereasarelabeledchatbotstillfollowsafixedscriptandhandsoffthe

momenttheconversationleavesthatscript.

Thegapbetweenthetwousuallystayshiddeninthesalesdemonstration,whichisbuilt

aroundasinglecleanscenario,andonlybecomesclearinliveoperationacrossthefullrangeofmessyreal-worldcontacts.

Buyersshouldtreattheword“agent”asthestartofaconversationratherthantheanswertoone,andseeaprospectivesystemactonarealworkflowbeforeacceptingthelabel.

HOWAGENTICAIWORKS

AgenticAIisnotsimplyamoreadvancedchatbot.ItrepresentsafundamentalshiftinthewayAIsystemsoperate,movingfromreactionandcontentcreationtoautonomoustaskexecution.

ThissectionexplainshowagenticAIworksatasystemlevelandhowitcomparestoother

commonAItypesincontactcenters:conversationalAIandgenerativeAI.AgenticAIoperatesasanautonomoussystemcapableofpursuingobjectives,makingdecisionsandacting

acrosstoolsandenvironments.

Itsarchitectureincludesseveralinterlockingcomponents:

•GoalDefinition:AIdetectstheuser'sobjectivefrominput(text,voice,orAPI)usingnaturallanguageunderstanding(NLU)orintentrecognition.Forexample,fromthequery“Iwaschargedtwiceformylastorder,”theAIidentifiesagoal:resolvebillingdiscrepancy.

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•Planning&Reasoning:thesystemformulatesamulti-stepplantoreachthe

identifiedgoal.Itthenusesplanningalgorithmsorreasoningframeworksto

determinethenecessarystepsandtheirsequence.Forexample,Authenticate>

Retrieveorder>Checkduplicatepayment>Processrefund>Notifyuser>Logaction.

•ToolandAPIIntegration:agenticAIinteractswithexternalsystemssuchasCRM,billingplatformsandschedulingsoftwareviaAPIsinordertocompletetasks.Unlikechatbots,whichonlysurfaceinformation,agenticsystemsexecutetransactionsandmodifydata.

•ExecutionandOrchestration:executeseachstepinreal-time,adjustingasneeded.Itmayoperateacrosschannelsandtouchpointsasrequired.

•FeedbackLoopsandLearning:outcomesaremonitoredandstrategiesadaptedbasedonsuccess/failuresignals,whichcanincorporateuserfeedback(e.g.

satisfactionscoresorcomplaints)toretraincomponentsoradjustworkflows.

•Human-AICollaboration:agenticAIknowswhentoescalate,pauseorrequest

humanvalidationthroughconfigurableconfidencethresholdsorifcertainconditionsaretriggered,enablingHuman-in-the-Loop(HITL)interactionsforcriticalsteps.

Figure3:TheagenticAIoperatingloop

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AgenticAIdiffersfromconversationalandgenerativeAIinnumerousways,includingautonomy,outputs,planningabilityandusecases.

Figure4:Aspectsofagentic,conversationalandgenerativeAI

Aspect

ConversationalAI

GenerativeAI

AgenticAI

PrimaryFunction

Dialogueandintentunderstanding

Contentcreation(text,images,code,etc.)

Taskorchestrationandautonomousexecution

OutputType

Textreplies,

clarifications

Summaries,messages,documents

Actionstaken,taskscompleted

LevelofAutonomy

Reactive

Semi-proactive

Fullyorsemi-

autonomous

UseCaseExamples

FAQbots,IVRmenus

Draftingfollow-up

emails,summarizing

calls

Resolvingtickets,processingrefunds

ContextHandling

Turn-based

conversation

Context-awaregeneration

Persistent,goal-andstate-drivenmemory

ToolUse

Noneorlookup-only

Referencecontenttocreateoutput

Executesacross

multiplesystemsand

APIs

PlanningAbility

None-scriptedflows

Noneorshallow

Multi-stepplanningandsequencing

ExampleOutput

“Letmetransferyouto

thebilling

department.”

Draftedrefundemail

Refundprocessed,

CRMupdatedanduser

notified

14

AGENTICAICHARACTERISTICS

AgenticAIisthenextevolutionarystepincontactcenterautomation,goingbeyondunderstandingandresponding.

AgenticAIsystemsplan,decide,actandlearn,differingfundamentallyfromconversationalandgenerativeAIinthattheAIagentdoesn’tjustspeakorwrite,itactuallygetsthingsdone,autonomouslyandacrosssystemsifnecessary.

SomeelementsofagenticAIinclude:

•SystemIntegration:operatesacrossmultipletoolsandAPIs,takingactionssuchasissuingrefunds,reschedulingappointmentsorupdatingrecords.

•CollaborationandDelegation:co-ordinateswithotheragentsorhumanworkers,usingconfidencethresholdstodecidewhentoself-resolveorescalate.

•PlanningandSequencingActions:agenticAImapsoutthestepsrequiredtoreachanobjective,forexample,authentication,followedbydataretrieval,followedbypolicyapplication,resultinginaresolvedissue.

•EnvironmentReasoning:understandsthestatusofexternalsystemsandconditions(suchasserviceoutagesorcalendaravailability),adaptingitsstrategyiftoolsordatasourcesareunavailable.Forexample,iftheCRMsystemisdown,AIcanroutethe

interactiontoahumanagentorlogatasktoretrylater.

•ErrorRecovery:detectsfailuresintheworkflowandtriestoreroute,escalateorretryasappropriate(e.g.ifpaymentprocessingfails,AIcanretrywithabackupsystemoralertasupervisor).

•AgentCoordination:inmulti-agenticsystems,AIagentsassignedtodifferent

domains(billing,techsupport,sales),collaboratewitheachother,passingondata,

sharingtaskownershipandaligninggoals.Inamulti-agenticenvironment,abilling

agentmayconfirmrefundeligibilityandthenpassthecasetoacomplianceagentforpolicyreview.

•PersistentMemory:storesknowledgeacrosssessionsandinteractions,enablingpersonalizationandcontinuity(e.g.rememberingacustomer’spreferredcallbacktimefromaprevioussession).

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AGENTICAIDEPLOYMENTMODELS

IndustrydeploymentsofagenticAItypicallyfallintooneoftheseoperationalmodels:

•MonolithicAgent:asingleagenthandlestasksend-to-end.Thisissimplertodeploy,butbydefinitionlessmodularandhardertoscaleacrossroles.

•Planner/ExecutorModel:whileoneagentplans,othersexecutesubtasks.Thismodelismoreflexibleandallowstargetedoptimization.

•Multi-AgenticSystems:thismodelusesmultiplespecializedagents,operatinglikeadigitalteam.Itismodular,scalableandidealforcomplex,high-volumeenvironments(e.g.telecomsorhealthcare).Forexample,oneagenthandlescustomer

authentication,anotherprocessesrefunds,whileathirdmanagescompliancelogging.

•Human–AIHybrid:combinesautomationwithhumanoversightatcheckpoints.Itiscurrentlythemostcommonenterprisedeploymentmodel,balancingrisk,control

andefficiency.

Figure5:CommonagenticAIdeploymentmodels

Regardlessofdeploymentmodel,thereisagreatemphasison“GovernanceFirst”agenticAIdeployments,whichaimstoprotectcustomers,dataandtrust.Thereisasectionlaterinthereportthatlooksatthis.

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OPENSTANDARDSANDINTEROPERABILITY

ThesectionabovedescribesagentsreachingexternalsystemsthroughAPIsand,inmoreadvanceddeployments,workingalongsideotheragents.

Untilrecentlyeveryoneofthoseconnectionshadtobebuiltandmaintainedindividually,whichtiedbuyerstightlytowhicheversupplierhaddonetheintegrationwork.Twoopenstandardsthathaverisentoprominencewithinthepastcoupleofyearsarechangingthat.

TheModelContextProtocol(MCP)givesanagentacommonwaytoreachthetools,data

andsystemsitneeds,soasingleconnectorcanservemanydifferentagentsinsteadofbeingwrittenfromscratcheachtime.

TheAgent2Agentprotocol(A2A)addressesthelevelaboveitandallowsagentsbuiltby

differentsupplierstodiscoveroneanotheranddelegateworkbetweenthem.BothhavebeenplacedunderneutralgovernanceattheLinuxFoundationandadoptedbythemajormodelandplatformproviders,whichiswhatgivesthemweight.

Figure6:HowMCPandA2Afittogether

Forcontactcenterbuyersthesignificanceisstraightforward:standardsofthiskindreducethecostofswitchingandmakeitrealistictoassembleanoperationfromthebest

componentsavailableinsteadofacceptingasinglesupplier'sentirestack.Procurementteamsshouldnowbeaskingwhetheraprospectivesolutionsupportstheseprotocols,becausethatsupportisbecomingafairproxyforhowopenaplatformreallyis.

17

USECASESFORAGENTICAI

AgenticAIextendsfarbeyondstaticchatbotsorreactiveassistants,actingasanautonomousagentthatunderstandsgoalsandexecutesacrosssystems,adaptinginrealtimetoachieveoptimaloutcomes.

ThefollowingagenticAIusecasesarecategorizedintothreepillars:liveagentassistance,customerself-service/digitalchannelsupportandanalytics/compliance.

AI-ENABLEDLIVEAGENTASSISTANCE

AIoffersgreatopportunitiesforareductionintalktimeandthereforecostandqueuelengthreduction,withoutnegativelyimpactingcustomerexperienceoroutcomes.

Thefollowingchartshowstheseeminglyinexorableriseincallduration.

Calldurationisalessimportantmetricnowthanhistorically,ascontactcentershaveallowedcalltimestoincreaseascustomerexperiencebecomesmoreimportantandself-service

takesupagreaterproportionoftheeasiershortcalls.However,queuelengthsimpactgreatlyoncustomerexperience:ifagentsaretalkingtocustomersforlonger,theycan’tbetaking

newcalls.

Figure7:Historicalmeancallduration(service&sales),2012-2025

Callduration(seconds)

408

381367

20142015

420

346

360

300

306

240

180

120

60

0

2012

Historicalmeancallduration(service&sales),2012-2025

447

410

370372

553

529

513

442

426423

392

347

2013

2016201720182019202020212022202320242025

Sales

Service

487

463

660

600

540

480

507

467

384

360

456

476

576

520

514

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Driveninpartbytheselongercalls,thefollowingchartshowsaveragespeedtoanswerandcallabandonmentrate,bothofwhichhaveadiscernibleimpactonCX,salesopportunitiesandcost.

Figure8:Historicalaveragespeedtoanswer&callabandonmentrate,2012-2025

Speedtoanswer(seconds)

Callabandonmentrate(%)

120

100

80

5.4%

60

40

20

31

0

2012

7.3%

6.0%

53

46

20152016

7.1%

6.1%5.7%

101

75

56

6.3%

5.3%

43

34

20132014

7.1%6.3%

99

79

73

5.9%

5.4%

60

50

20172018

10.0%

9.0%

8.0%

7.0%

6.0%

5.0%

4.0%

3.0%

2.0%

1.0%

0.0%

Historicalaveragespeedtoanswer&callabandonmentrate,2012-2025

7.3%

74

AveragespeedtoanswerCallabandonmentrate

2019202020212022202320242025

8.9%

AgenticAIcantrimtimeandmoneywhichiscurrentlywastedby:

•searchingfortherightinformation

•accessingmultipleapplicationsandscreens

•pausesforagentstotype

•longandinaccuratepost-callwork.

Findingtherightinformation

AIcanprovidetheagentwithsuggestionsaboutnextbestaction,pulluprelevant

informationfromtheknowledgebase,makesuggestionsbasedoncustomerhistoryandsentimentaboutoptimalcross-sellingandupsellingopportunities,andeventhestyleofconversationthatthiscustomermayprefer.

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Apartfromcuttingdownonwastedtime,thisalsohasapositiveimpactonfirst-contact

resolutionandcustomerexperience.Thisisofparticularusetolessexperiencedagentsandforunfamiliarsubjectareas.

AgenticAImonitorsthereal-timedesktopandvoicedata,triggeringprocessessuchas

informationprovisionandback-officeprocesses.Itthenexecutesback-endactionsonbehalfofagents.

Forexample,whilethehumanagentcontinuestheconversation,theAIagentcanbeissuingarefundorupdatinganaddressdrivenbywhat’shappeningwithintheconversation.This

reducescognitiveloadonthehumanagent,aswellascuttingcalldurations.

Itcanalsoprovidecoachingoralertsifthere’salengthypauseintheconversationor

anythinghasbeendonewrong.Agentscanalsousespecificphrases,suchas“I’lljustlookthatupforyou”,triggeringtheAIassistanttotakeactionandputtingtheinformationonasingleagentdesktopapplication.

Thiscontextualknowledgeretrievalgathersrelevantknowledgebasearticlesorhistorical

casedatabasedonliveconversationandsurfacesstep-by-stepguidancetailoredtothe

currentinteraction.Forexample,iftheagentistalkingtoalong-termcustomer,theAIagentcanshowtheirloyaltytierandpolicyexceptionswithoutthehumanagenthavingtosearchforitthemselves.

AIcanworkalongsideagentstoproviderelevantknowledgethatmaybeotherwisetakealongtimetofind.ItcanalsoupdatetheknowledgebasesavailabletohumansandAIself-servicesystemsusinganautomatedfeedbackloopthatisconstantlyimprovingbasedonactualoutcomes.It’salsoimportanttonotethatagenticAIassistsacrossmultiplechannels,suggestingactionsinonechannelwhilecompletingtasksinanother(e.g.sendinga

confirmationemailduringalivechat).

Accessingasinglescreen

Manyoftoday’scontactcentersusecomplicated,multipleapplications,oftenonlylooselylinked,whichrequireskilledandexperiencedagentstonavigate,letalonetomanage

interactionwithcustomerssuccessfullyatthesametime.

Inmostcaseswherecomplex,multipleapplicationsareused,theyarenecessaryfortheagentstodotheirjob,sothequestionisnot“Howcanwereducethenumberof

applications?”,butrather“Howcanweimprovehowtheagentusestheapplications?”.

Atthemoment,duetocomplexity,expenseandthesheerweightofconstantchange,

applicationsareeitherintegratedveryloosely,ornotatall.Agentsaretrained(ormorelikely,learnonthejob)toswitchrapidlybetweenapplications,relyingontheirexperiencetomakesuretheydon’tforgettodowhat’srequired

Manycontactcentersstillrelyoninformationheldinlegacysystems.Agentsuseanaverageof3.5applicationswithinacalland2.3post-call,whichleadstoconsiderableamountsoftimebeingspent–especiallybyinexperiencedagents–tryingtofindtherightinformationor

inputdataonthecorrectscreen.

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Therearesignificantissuesaroundnotaskingorforgettingtokeyininformation,failingtoinitiatethecorrectfollow-onprocessesortypeinconsistentdata,whichcanoftenleadtounnecessaryrepeatcalls.Theuseofmultipleapplicationshasanegativeeffectontrainingtimesandaccuracyratesfornewagentsaswell.

AgenticAIsolutionscanremovetheneedforagentstologintomultipleapplications,aswellasassistingthemwiththenavigationbetwee