【FFA 2026】平台AI实践 Flink Agents在LinkedIn的 探索和实践

FlinkAgents在LinkedIn的探索和实践

FlinkAgentsatLinkedIn:EarlyEnterpriseExplorationandtheRoadAhead

演讲人AlanZhang,StaffSoftwareEngineer,LinkedIn

演讲人WeiqingYang,SeniorStaffSoftwareEngineer,LinkedIn

01

Motivation

TheGap–StreamingDatavs.AutonomousReasoning

SituationBefore

●StreamingsystemspowerFeed,Ads,andSearch

●Thetradeoff:Builtforspeed,notforautonomousreasoningorreal-timedecision-making

TheEngineeringBottleneck:ContextPersistence

●Managingshort-andlong-termmemory

●Handlingstaterecoveryduringfailures

●Sharingcontextacrossmulti-agentworkflows

TheBreakthrough–LinkedIn’sFirstStreamingAIAgents

WhatWeDid:LaunchLinkedIn’sfirstStreamingAIAgentonApacheFlink

ContinuousIntelligence:Combiningreal-timestreamswithagenticworkflows

●Observe:Monitorlivedatastreams

●Reason:ApplyLLMsforcontextandintent

●Act:Executeautonomouslywithininfrastructure

TheImpact:

●Smarterautomation

●Faster,adaptivedecision-making

●Enterprise-scalememberexperiences

02

WhatisFlink-Agents

FlinkAgentsFrameworkArchitecture

WhyFlink-Agents:MemoryasaFeature

NativeMemorySupport

●EmbeddedRocksDB:Manageslocalstateforsub-millisecondreasoningandflawless

pause/resume

●Mem0Integration:Natively

supportsLong-TermSemanticMemoryforRAGandhistoricalcontext.

Event-Driven&Scalable

●Proactive:Builtforevent-

drivenworkflowsratherthanpassivequerying

●Self-HealingatScale:Nativelyhandlesmassivescaleusing

Flink’srobustcheckpointingmechanism

WhynotOthers?

●TrueStreamProcessing:

Offersfirst-class

windowingandaggregation

●ProvenTechStack:

Leveragesinfrastructure

alreadydeeplyverifiedandadoptedatLinkedIn

LinkedIn'sFirstStreamingAIAgents

LinkedInContributionstoApacheFlinkAgents

Models,Retrieval&

Integrations

Expandingtheframework'sreachacrossLLMs,embeddings,and

externaltools

●Chat-modelintegrations:

Anthropic,OpenAI,AzureOpenAI

●Vector-store&embeddingstackwithbuilt-inRAGretrieval

●MCPtoolsupport,eventlogging,andricherdocs&examples

Runtime,State&

Reliability

Hardeningdurableexecutionand

exactly-oncecorrectnessforlong-runningagentworkloads

●Refactoredthecoreaction-executionoperator

●Checkpoint-safeagentmemory&state-correctnessfixes

●Configurableevent-logobservability

03

LinkedInEcosystemIntegration

LinkedInEcosystemIntegration:Whatwevalidated

●Runtimecompatibility

○FlinkAgentscanrunasPyFlink+Flinkjobsinourmanagedenvironment

●Platformintegrationpath

○AgentjobscanfollowthesamemanagementpathasotherFlinkjobs:

controlplane→jobCR→FlinkKubernetesOperator→Kubernetes

●Ecosystemconnectivity

○AgentscanreuseFlinkconnectorstoconsumeandproducedataacrossLinkedIn’sinternaldatasystems

ArchitectureOverview:AgentsasmanagedFlinkjobs

04

PatternsfromLinkedInExploration

Pattern1:ProactiveDiagnosisAgentforFlinkJobFailureSignals

Currentworkflow:symptom-basedalertstriggerhumaninvestigation

OnLinkedIn’smanagedFlinkplatform,thousandsofjobsemithealth,lag,andotheroperationalmetrics.Defaultalertsprotectthesejobs,butwhenanalertfires,itusuallypagesthejobownerfirst,evenwhentherootcauseisplatforminfrastructure.

PainPoint:Alertingisautomated,butdiagnosisisstillhuman-triggered.

Pattern1:ProactiveDiagnosisAgentforFlinkJobFailureSignals

Newarchitecture:event-triggereddiagnosisonfailurestreams

EachmanagedFlinkjobemitsenrichedfailureeventsintoacentralizedKafkatopic,whichtriggersanagent-baseddiagnosispipeline.

Failureeventsnowtriggerdiagnosisdirectly,insteadofwaitingforsymptom-basedalertsandmanualescalation.

Pattern1:ProactiveDiagnosisAgentforFlinkJobFailureSignals

WhyFlinkAgentsfits:reasoningafterstreamprocessing

ThehardpartisnotcallinganLLM.Itiscontinuouslyprocessingnoisyfailurestreams,reducingthemintomeaningfuldiagnosistriggers,andkeepingthepathopenforsafeautomation.

01

ContinuousSignals

watchfailuresastheyhappen

Failureeventsarrivecontinuouslyacrossthousandsofmanaged

jobs.

→02

StreamProcessingFirst

dedup,filter,correlatebeforeLLM

Mostsignalsarenoisyor

repeated.Flinkdedups,filters,androutesonlymeaningfulcasesto

agents.

→03

MultipleAgentReasoning

triage+diagnosiswithtools

Triageanddiagnosisaredifferentresponsibilities,withdifferent

context,tools,andskills.

→04

SafeActionPath

diagnosistoday,guardedremediationtomorrow

Today:adiagnosisreport.Future:guardedremediationwithreplay-safeactions.

Thispatternisnot“askanagentafteranincident.”Itis“letfailurestreamsproactivelytriggerdiagnosis.”

Pattern2:Always-CurrentJobContextforAgenticDiagnosis

Currentworkflow:agentsrebuildjobcontextoneveryinvestigation

Foranyjobdiagnosisagent,onefailureeventisnotenough.Theagentneedsalways-currentcontextaboutwhatthejobdoes,whatchangedrecently,whatdependenciesithas,andwhathappenedbefore.Today,thiscontextisscatteredacrossdashboards,configs,deploymenthistory,logs,incidents,andoncallnotes.

PainPoint:Theagentcanreason,butitfirsthastorebuildthejobcontextfromscratch.

Pattern2:Always-CurrentJobContextforAgenticDiagnosis

Newarchitecture:maintainjobcontextasastreamingmemoryplane

Insteadofrebuildingcontextduringeveryinvestigation,maintainanalways-currentjobprofilefromeventstreams.AFlinkAgentspipelinecontinuouslyjoinsdeployment,failure,incident,performance,andmaintenanceeventsperjob,summarizesthemwhenneeded,andexposesapre-joinedjobprofiletodiagnosisagentsthroughaqueryinterface.

Movejobcontextfrom“rebuiltduringdiagnosis”to“maintainedcontinuouslybythestream.”

Pattern3:GuardedSupervisorAgentforCross-SystemSelf-Healing

Currentworkflow:domainagentsarepowerful,butcoordinationisfragmented

Enterprisesmayalreadyhavemanydomainagents.Theseagentscanreasonandactwithintheirowndomain,butincidentsofteninvolveshareddependenciesacrosssystems.Withoutacoordinationlayer,eachagentworksfromitsownsignalsandmayinvestigateoractindependently.

PainPoint:Domainagentsareusefulspecialists,butself-healingneedssharedcoordination,memory,andguardrails.

Pattern3:GuardedSupervisorAgentforCross-SystemSelf-Healing

Newarchitecture:FlinkAgentsastheevent-drivensupervisorlayer

UseFlinkAgentstobuildaguardedsupervisorthatcontinuouslylistenstocross-systemfailureevents,correlatesrelatedsignalsintoincidents,

coordinatesexistingdomainagentsthroughstandardinterfaces,maintainsincident-levelmemory,anddispatchesactionsonlythroughsharedguardrails.

FlinkAgentsbecomestheevent-drivencoordinationandguardraillayer,notareplacementforeveryagent.

05

LessonsLearned&RoadAhead

LessonsLearned

●Streamprocessingshouldcomebeforereasoning

○Per-eventLLMcallsdonotscaleunderrealQPS,latency,quota,and

costconstraints.UseFlinktofilter,dedup,window,batch,andcorrelateeventsbeforeinvokingthemodel

●Usestagedagentsandmodelrouting

○Lightweighttriageagentshandlequickevaluation;strongermodelsarereservedforcomplexdiagnosis

●Guardrailsmustbedesignedfromdayone

○Oncediagnosismovestowardself-healing,ratelimits,bla