AI对广域网的影响：思科2026年报告

AIImpactonWide

AreaNetworks

CiscoReport2026

©2026Ciscoand/oritsaffiliates.Allrightsreserved.

AIImpactonWideAreaNetworks:Cisco2026report

Contents

Executivesummary 4

KeyFindings 4

Takeaways 7

Introduction 8

Dataanalysisandfindings 9

Datavolumegrowth 9

Dataflowlength 11

Dataflowrate 12

Trafficasymmetry 13

Networklatencyimpact 14

AgenticAItrafficpatterns 15

Lookingahead 18

EnterpriseusageofagenticAIandnetworkimpact:2026-2035 18

ConsumerUsageofAIandAgenticAIandnetworkimpact:2026-2035 21

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AIImpactonWideAreaNetworks:Cisco2026report

Aboutthereport

TheAIImpactonWideAreaNetworksreportrepresentsthekeyfindingsandtakeawaysfromaresearch

initiativeledbyCiscoSystems,Inc.Thereportcombinesreal-worldtrafficanalysis(usingtheassurancetool

CiscoCrossworkAssuranceUserExperience

),third-partyindustrydata,andCiscocontrolledlabtestsofAIagents.ItexploreshowAI-driventrafficdiffersfromtraditionalnon-AIwebtraffic,withafocusontheimpactofagenticAItrafficandtheimplicationsfornetworkdesign,capacityplanning,andassurance.

Whatsetsthereportapartisit’sbasedonreal-worldtrafficdataincludinganearlylensonagenticAItraffic(currentlysmallbutgrowingfast)thatletsusstarttoseeandmeasureanewclassofAInetworktrafficandunderstandtheimplications.Thereportincludes:

•DirectmeasurementofliveAIinferencetrafficacrossserviceprovidernetworks

•EmpiricaltestsofAItrafficcharacteristicstotrainmodelsthatidentifyandtrackAIflowswithprecision

•ArepeatablemeasurementframeworkandbaselinetotrackAItrafficevolutionandforecastingonanannualbasis

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AIImpactonWideAreaNetworks:Cisco2026report

Executivesummary

Therapidadoptionofartificialintelligence(AI)—particularlyAIinferenceandagenticAIsystems—isbeginning

toreshapenetworktrafficpatternsinmeasurableand

structurallyimportantways.By2027,80%ofexecutives

believetheircompany’scompetitivesurvivalwilldependonagenticAI,

1

andconsumerusageofAIisalreadyprevalentandaccelerating.Thisisdrivingafundamentalshiftinhowtrafficisgenerated,distributed,andexperienced,with

implicationsforserviceprovidersandenterprisesthatmanagelarge-scalenetworks.

KeyFindings

Thefutureofconnectivityisnolongerjustaboutfasterdownloads—it’saboutthevital

“spinalcord”ofintelligencebetweenagentsandAImodels—acriticaldependencythat’s

forgingnewpathsofcommunicationsthatimpactshowwemanagenetworks.While

theindustryhasspentdecadesoptimizingthenetworkforhuman-paced,burstyvideo

streams,theriseofagenticAIischangingnetworktrafficprofilesandbehavior.By2035,one-quarterofnetworktrafficisprojectedtobeAIinference(Source:Ciscomodel).Theseflowsdon’tbehaveliketheweb.Theylivelonger,demandmoreupstreamcapacity,and

operateatsoftwarespeed,nothumanspeed.TheconnectivitybetweenagentlogicandAImodelseffectivelybecomestheagent’s“spinalcord”—acriticaldependencywherebyanynetworkdegradationdirectlyimpairsagentfunctionality.

1CiscoreportTheRacetoAgenticAI:WhyInfrastructurewillmakeorbreakworkforceintegration

/c/dam/en/us/solutions/collateral/artificial-intelligence/race-to-agentic-ai-report.pdf

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AIinferencetrafficissmalltoday—butisgrowingextremelyfast.

WhileAIinferencetrafficremainsnegligiblecomparedtodominant

categorieslikevideostreaming,observedgrowthratesareexceptional.Token-consumptiondatashowsnearly10xyear-over-yeargrowth,whileinsomeserviceprovidermeasurementsweareseeingaround4xgrowthinjusteightmonths.SustainedgrowthattheseratesmeansAItrafficwillbecomeameaningfulcomponentofoverallnetworktraffic

by2035.

AIinferencetrafficflowsbehavedifferentlyatthetransportlevelvs.typicalwebtransactions.

•AIinferenceflowslastapproximately2xlonger,mainlyduetotoken-by-tokencontentgeneration.

•Medianflowratesofregularwebtransactionsare10xlargerthanAIinferencetrafficflowswhichhavesmoothersustainedthroughput,ratherthanburstydelivery.

Thesecharacteristicswillrequireflow-awarenetworkandsecuritysystems,includingfirewalls,intrusiondetection,andDeepPacket

Inspection(DPI),tohandlegreaterscale,distribution,andstateefficiencyovertime.

AIImpactonWideAreaNetworks:Cisco2026report

Tokenconsumptiondata

10x

year-over-yeargrowth

AIinferenceflows

2x

longer

Flowrateforwebvs.AItraffic

10x

larger

Trafficsymmetrystatusquoischanging.

9%

upstream-heavyAIinferenceflows

AIinference—andespeciallyagenticAI—introducessignificantlymoreupstreamtrafficduetolarge,context-richprompts.

•Approximately9%ofAIinferenceflowscarrymoreupstreamthandownstreamtraffic,versusabout0.5%fortypicalwebtraffic.

Thistrendisexpectedtointensifyasagentsmaintainandtransmit

growingcontextualstate,withdirectimplicationsforaccessplanningandupstreamcapacitymodeling.

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Networklatencyisnotthedominantbottleneck—yet.

End-to-endAIinferencelatencyisdrivenprimarilybymodelprocessing,typicallyrangingfromhundredsofmillisecondstoseveralseconds.

Networklatencyiscurrentlysmallenoughanddoesnotyetjustify

large-scalearchitecturalshiftstowardedgeinferencepurelyforlatencyreasons.Inferencehardwareimprovementswillleadtonetworklatencybecomingmorerelevant,andwearealreadyseeingproofpointsofthat.MeasuringandassuringtheAIinferenceexperienceisbecomingcriticalforperceivedservicequality.

AIImpactonWideAreaNetworks:Cisco2026report

#s~5secondsforAIinference

latencyvs.~20-50milliseconds

fornetworklatency

AIagents

450%

moretotaltraffic

70%

ofthattrafficisAIinference

AgenticAIfundamentallyamplifiesnetworkusage.

AIagentsactasnetwork“powerusers,”operatingatsoftwarespeedratherthanhumanspeed.Empiricaltestingshows:

•Upto450%moretotaltrafficisgeneratedpertaskwhenperformedbyanagent.

•Approximately70%ofthattrafficisAIinference,makinginferencepathsmission-critical.

•TheconnectivitybetweenagentlogicandAImodelseffectively

becomestheagent’s“spinalcord”—acriticaldependencywherebyanynetworkdegradationdirectlyimpairsagentfunctionality.

AItrafficimpactby2035*

Enterprisenetworks:

AIandagenticAI

63%

additionalgrowthnetworktraffic

WithoutagenticAI,enterprisetrafficisprojectedtogrowbyabout2.5x

overthenextdecade.WithagenticAIadoption,trafficgrowthcould

increasebyabout9x,drivenbyautonomoustaskexecutionandinference-heavyworkflows.

Consumernetworks:

ConsumerAIadoptionisacceleratingandwillreachnear-universalusagebythemid-2030s.AIandagenticAIareprojectedtoincreaseconsumer-drivennetworktrafficbyabout6.6x,representingabout63%additional

growthcomparedtonon-AIscenarios—makingthisthedominantdriverofoverallinternettrafficexpansion.AIinferencetrafficisprojectedtobe25%oftotalnetworktrafficby2035.

*Source:Ciscomodel

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Takeaways

•AIandagenticAIwillnotjustincreasetrafficvolume—theywillchangetrafficshape,symmetry,duration,andcriticality.

•AIinferencepathswillbecomestrategicnetworkassets,requiringhighlevelsofresilience,observability,anddifferentiatedtreatment,forexample,QualityofService(QoS)andpathsecurity.

•Traditionalplanningassumptions(burstiness,downlinkdominance,human-pacedinteractions)willneedtoadapttothenewrealityofAIandagenticAItrafficthatlastslonger,demandsmoreupstreamcapacity,andoperatesatsoftwarespeed.

•ContinuousmeasurementofAI-specificKeyPerformanceIndicators(KPIs)andadaptivenetworkarchitectureswillbeessentialtosustainexpectationsforperformanceanduserexperience.

ThisreportmarksthefirststepinongoingresearchontheimpactofAIonnetworktraffic.AsAIadoptiondeepensacrossenterprisesandconsumers,futureresearchworkwillrefineprojections,validateemergingpatterns,andguideoperatorsinevolvingtheirnetworksforanAI-drivenworld.

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Introduction

□

AdoptionofAIandAI-basedapplicationssuchasAIagentsisgrowingataveryfastpace.InaCiscoreportwithOmdia,80%ofexecutivesviewagenticAIascriticaltocompanysurvivalby20272.OtherindustryreportsconfirmsimilargrowthprojectionsforbothenterprisesandconsumerAIapplications.Inthiscontext,serviceprovidersandorganizationsrunninglarge-scalenetworksneedtoevaluatehowtheproliferationofAIisgoingtoimpactnetworksin

termsoftrafficgrowth,patterns,orothercharacteristics.

Thisreportisthefirstinanannualserieswherewewillsharekeyfindingsandimplicationstohelpnetworkleadersmakedecisionsontheevolutionoftheirnetworkinfrastructure.CiscohasinitiatedresearchonAItrafficpatternsandhowtheydifferfromnon-AIweb-based

transactions,withaspecificlensonagenticAItrafficandapplications.Thedatapresentedinthereportisbasedonrealtrafficdataanalysisfrom

CiscoCrossworkAssuranceUser

Experience

,Ciscoempiricaltests,andthird-partyreports.

2CiscoreportTheRacetoAgenticAI:WhyInfrastructureWillMakeorBreakWorkforceTransformation.

/c/dam/en/us/solutions/collateral/artificial-intelligence/race-to-agentic-ai-report.pdf

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Dataanalysisandfindings

Datavolumegrowth

Thefirstquestionsnetworkoperatorsaretryingtoanswerare“HowmuchAItrafficwillexist?”and“Howwillthistrafficimpactournetwork?”

AItraffic,inthiscase,referstoAIinferencetrafficwhichflowsacrosstheWANbetweenthedevicerequesting

modelinferenceandthedatacenterorlocationwherethemodelishostedandtheinferencehappens.Insidedatacenters,terabytesoftrafficflowsarehappeningduringAImodeltraining,butthattrafficisnotcrossingtheWAN,exceptforlargedatatransfersforAItrainingpurposes.

TheactualvolumeofAIinferencetrafficisstillnegligiblecomparedwithothertypesoftraffic,suchasvideostreaming,andwilllikelyremainverylowinrelativetermsinthenearterm.However,thereareverysignificantgrowthrates:

•Tokenconsumptiongrowthof10xyear-over-yearinOpenRouterAIfromDecember2024toDecember2025.)3

LLMLeaderboard

TokenusageacrossmodelsonOpenRouter

8T

6T

4T

2T

Jan13,2025Feb17

Mar24Apr28Jun2Jul7Aug11Sep15Oct20Nov24Dec29

Figure1.OpenRoutertokenconsumptionCY2026

Source:Openrouter.ai

3OpenRouter,LLMLeaderboard.

https://openrouter.ai/rankings#leaderboard

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TrafficanalysiswithdatafromtwoserviceprovidersshowsconsistenttrafficgrowthforAIinferenceoverasimilarperiod:4xtrafficgrowthoveraperiodofeightmonthsoftrafficmonitoring.

total_volume(bytes)

TotaltrafficvolumeLLMtraffic

1e7

8

total_volume

Trend

7

6

5

4

3

2

2025-042025-052025-062025-072025-082025-092025-102025-112025-12

Timestamp

Figure2.AIinferencetrafficvolumeevolutionSource:Ciscodata

AIinferenceservicesareusingbothTransmissionControlProtocol(TCP)-andQuickUDPInternetConnections(QUIC)-basedtransport.Thesplitintermsofnumberofflowsisalmost50%,whileintermsofdatavolumeQUICrepresents57%.

Implications:WhileAIinferencetrafficisstilllowinabsoluteterms,sustainedhighgrowthrateswillresultintoarelevantportionofalltrafficovertime.Clearly,theadoptionofAIbyapplicationsandusersisdrivingfast

growth.TheadoptionofQUICastransportforAIinferencepresentsencryptionchallengesforDPIsystemstodetecttrafficattheapplicationlayer.

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Dataflowlength

AIinferenceflowspresentdifferentcharacteristicscomparedtonon-AIwebtraffic.Whilenotdramatic,thesedifferencesmayimpactcapacityplanning.AnalysisofdataflowlengthshowsdifferencesbetweenAIinferenceflowsversusregularwebtransactions.

Statistically,AIinferenceflowslast2xlongerthanregularwebtransactions.

643.00

1292.00

Normalizedfrequency

0.05

0.00

05000100001500020000

Flowduration(ms)

0.20

LLMflow

non-LLMflow

Median-LLMflow:1292.00

Mediannon-LLMflow:643.00

NormalizedHistogramofflowduration(ms)

0.15

0.10

Figure3.FlowdurationdistributionofAIinferenceflowsvs.non-AIwebtransactionsSource:Ciscodata

ThemaindriveristhewayAIinferencetrafficgeneratescontent,onetokenatatime,resultinginlongerandlowerrateflowscomparedtootherflows.

Implications:For“flow-aware”networksystemsthatmustkeepstateforflowsintables,theproliferationofAIinferenceflowsthatlastlongermeansgrowingflowtableswillneedtobeeffectivelyplanned.Overtime,securityandflow-awarenetworksystemsarelikelytobecomemoredistributedtocopewithforwardingstategrowth,similartotheapproachusedby

CiscoHypershield™

.

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Dataflowrate

Relateddirectlytotheflowlength,thedataflowrateforAIinferenceflowsshowsadifferentpatterncomparedtoregularwebtransactions.Medianflowrateis10xlargerforregularwebtransactionscomparedtoAIInferenceflows.

Normalizedfrequency

338.00

3803.00

1000015000200002500030000

Flowrate(Kbps)

0.05

0.00

05000

LLMflow

non-LLMflow

MedianLLMflow:338.00

Mediannon-LLMflow:3803.00

NormalizedHistogramofflowrate(Kbps)

0.25

0.20

0.15

0.10

Figure4.FlowratedistributionofAIinferenceflowsvs.non-AIwebtransactionsSource:Ciscodata

ThemainreasonforthisdifferenceistheprocessthatgeneratesthedatainAIinference.Adataflowthatoccursonetokenatatimeislikehavinga“shaper”appliedtothedataflow.Regularwebtrafficflowspeakmuchhigher,asthecontentcanberetrievedfromwhereveritisstoredanddeliveredtotheuser.

Implications:Differentmedianaverage-trafficandpeak-to-averageratesmayrequiredifferentQoSsettingsinthenetworktomanageAIinferencetrafficversusnon-AIinferencewebtransactions.

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Trafficasymmetry

PotentialchangesintrafficasymmetryareexpectedwithAIinferencetraffic.Networktrafficasymmetryvariesdependingonaccesstype(mobilevs.wirelinenetworks)andservices.Mobilenetworktraffictendstobemoreupstreamheavyduetosocialnetworking,withmorecontentsentupstreamcomparedtowirelinenetworks.

AnalysisofAIinferenceflowsversusnon-AIwebtransactionsshowscleardifferencesintrafficsymmetry.AsFigure5shows,inAIinferencetraffic,9%offlowshavemoreupstreamtrafficthandownstreamtraffic

comparedtoother

HTTPtransactions

,wherethisoccursinonlyabout0.5%oftheflows.

NormalizedFrequency

21.11

145.39

2505007501000125015001750

Flowsymmetry(downstream/upstreamration)

NormalizedHistogramofFlowsymmetry(downstream/upstreamratio)

LLMflow

non-LLMflow

MedianLLMflow:21.11

Mediannon-LLMflow:145.39

0.1

0.0

0

0.6

0.5

0.4

0.3

0.2

Figure5.TrafficflowsymmetryofAIinferenceflowsvs.non-AIwebtransactionsSource:Ciscodata

Weforeseethistrendfurtherconsolidatingandgrowing.ThereasonforthisdifferenceisthefactthatAI

inferencerequestsoftencaptureagrowingamountofcontextinthepromptinordertoproducearelevant

response.AsadoptionofagenticAIgrows,moresignificantchangestonetworktrafficsymmetrypatternscanbeexpected.Agentswillcapturecontinuouscontextastasksareexecuted,andthisstateneedstobeinsertedintotheprompts.

Implications:Particularlyforradiocapacityplanning,trafficsymmetryassumptionsarearelevantfactor.AsadoptionofAIinferenceandagenticAIgrows,itwillbeimportanttotracktheevolutionoftrafficsymmetry,asitwillcontinuedecreasingovertime.

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Networklatencyimpact

LargeLanguageModel(LLM)inferencerequeststendtohavesignificantlyhigherlatencythantypicalweb

APIcalls,andtheresponsetimescanbemorevariable.TraditionalwebapplicationRESTAPIsoftenstrivefor

subsecondorevensub-100-millisecond(ms)responsetimes.Forinstance,agoodAPIresponsetimeistypicallybelow100msinmanywebscenarios.Bycontrast,evenshortLLMqueriesincurresponsetimesofhundreds

ofmillisecondsjusttobeginproducinganoutput,andfullresponsesoftentakeseconds.LLMlatencycanbemodeledasfollows:

Latency=TimeToFirstToken(TTFT)+TimePerOutputToken(TPOT)×Numberofoutputtokens

TTFTistheinitialoverheadtoprocesstheprompt,andTPOTisthegenerationtimepertoken.Thismeanslatencygrowswithoutputlength.Forexample,OpenAIGPT-5.2

4

mighttakeontheorderof9mspertoken,andAnthropicClaudeOpus4.5about20mspertoken.A100-tokenGPT-5.2answercouldtakeabout1second,anda

maximum-length600-tokenanswercouldapproach5.4seconds.ThereiswidevariabilityamongLLMs,modelproviders,implementations,andthehardwareusedforinference.

Implications:AIinferencelatencyvarieswidelyfromafewhundredmillisecondstomultipleseconds.Networklatencyisstillatthispointaverysmallcomponentofthetotalend-to-endinferencelatency.Inference

hardwareis,however,evolvingandbecomingmuchfaster.Asinferenceaccelerates,thenetworklatency

componentplaysamorerelevantroleontheend-to-endperceivedexperience.Networklatencywillbecomeakeyfactorforinferencedistribution,combinedwithotherslikescale,datasovereignty,andsecurity.AdditionallyServiceProviderswillneedtomonitortheactualAIinferencelatencythatthecustomerisexperiencing,asit’sakeyfactorinperceiveduserexperience.WithCisco®CrossworkAssurance,itispossibletomeasurekeyAIinferenceKPIsacrossdifferentnetworkendpoints,alongwithnetworkandserviceKPIs.

4LLMLeaderboard—Comparisonofover100AImodelsfromOpenAI,Google,DeepSeek&Others.

https://artificialanalysis.ai/leaderboards/models

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AIImpactonWideAreaNetworks:Cisco2026report

AgenticAItrafficpatterns

InadditiontoAIInferenceflows,agenticAIasanAIapplication

deservesaspecialfocusdueto

itstransformationalimpactandtheimplicationsfornetworks.

AIagentsaresoftwareprocessesthatuseanAImodelasabraintomakedecisions.Then,dependingonthetypeofagent,multipletoolsareusedtocompletespecifictasksorgoals.

Therearethreemainsourcesordestinationsofdatainanagent(seeFigure6):

•Theagentitself(wheretheagentcoderuns)

•TheAImodel(whereAIinferencehappens)

•Thetoolsand/orsourcesofdatafortheagent

Webproxytool

websites

OpenAI

ODRAgent

Figure6.AIAgenttestingenvironmentSource:Cisco

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Forexample,weareusinganOpenDeepResearchagentfromLangchain5(otheragentswillbehavesimilarly).Let’sanalyzewhathappenswhenagiventaskisrequestedtotheagent(seeFigure7).

70%

LLMInferencetraffic

450%

Agent-driventrafficgrowth

26.8MB

Agenttriggeredtraffic

44

websourcesanalyzed

Task:"ForCisco,gatherpublicIR,socials,news,bets,partnerships,execbiosandsocialposts,

organization,announcements,competitive"

Datasources

OpenAI

5.9MB

11MBAgent

44differentsourcesexplored(viaWebproxytool)

5.9MB

74KB

3.8MB

Webproxy74KB

Figure7.AIagenttrafficflowsSource:Cisco

Arequesttoanagentgeneratesasignificantamountofnetworktrafficbetweenthedifferentcomponentsoftheagent.Overall,thenetworktrafficgeneratedortriggeredbytheagentismuchlargerthanthenetworktraffic

generatedifthesametaskwasperformedmanuallybyahuman.Inourexamplethereisa450%increaseintrafficassociatedwithusinganagent,andnotably,70%ofthatnewincrementaltrafficisAIinference.

ThefollowingarekeyconsiderationsrelatedtoAIagentbehavioranditsinteractionwithanddependencyonthenetwork:

•AIagentsactasnetworkpowerusers.Agentsconsumecloudandnetworkresourcesatsoftwarespeed,

comparedtowhenthesetasksareperformedmanuallyathumanspeed.Thisresultsinasignificantincreaseinnetworktraffic.

•AIinferencetrafficisnotonlyasignificantportion,butthecriticalportionoftrafficthatisnecessaryfortheagenttooperate.ThenetworkpathbetweentheagentcodeandtheAImodelcanbeconsideredtheagent’s‘spinalcord’.Anyfailureorconstraintinthatcommunicationspathwillsignificantlyimpacttheagent’sabilitytooperate.

5OpenDeepResearch,

/langchain-ai/open_deep_research/tree/main

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[MCP][MCP]LLM

[MCP]

Tool_ATool_BTool_C

LLMAgent

Agent’sSpinalCord

LLM

•[On-device]

•On-prem

•PrivateDC

•Publiccloud

•On-device

•On-prem

•PrivateDC

•Publiccloud

Thenewcriticalpath

Figure8.AIAgenttrafficpatternandnewcriticalpathSource:Cisco

Implications:AsagenticAIadoptiongrowsinbothenterpriseandconsumerapplications,networktrafficwillgrowaccordingly,primarilydrivenbytheadoptionofagentsinusertasks.Inthatprocess,AIinferencetrafficwillgrowsignificantlyasitisalargepercentageofAIagent-driventraffic.ResiliencyatthenetworklevelforcommunicationpathsiscriticalforAIinferenceacrosstheagent‘spinalcord’betweentheagentcodeandthemodel.

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Lookingahead

Thisreportexploresthebehavioraldifferencesandspecialcharacteristics