2026软件工程基准报告：AI生产力版 SOFTWARE ENGINEERING BENCHMARKS REPORT The AI productivity edition

SOFTWAREENGINEERINGBENCHMARKSREPORT

‘26

TheAI

productivityedition

CREATEDFROMASTUDYOF8.1+MPRSFROM

4,800ENGINEERINGTEAMSACROSS42COUNTRIES.

Tableof

contents

INTRODUCTION3METRICSDEFINITIONS9L’ST:FORAI?38

KNGINEERING5ETCK:SACCEPTANCE12APPENDIX40

KNNIZE6AIPRODUCTIVITYINSIGHTS14

2026BENCHMARKSREPORT3

Introduction

We’rethrilledtobepublishingtheifthannualeditionof

ourSoftwareEngineeringBenchmarksReport.The2026

reportisthemostcomprehensiveanalysisofitskind,built

fromover8.1millionpullrequestsacross4,800developmentteamsin42countries.Thisyear,ourresearchgoesbeyondtraditionalsoftwaredeliverymetricstoexploreoneofthe

mostimportantquestionsfacingengineeringleaderstoday:HowisAIimpactingsoftwaredevelopment?

DATASOURCEDFROM

4,800teams

8.1+millionpullrequests

42countries

Artiicialintelligencehasrapidlybecomeembeddedinthetoolsandworklowsthatengineersrelyoneveryday.

AI-assistedcodinghasincreasedoutputacrosstheindustry,butithasalsoexposednewchallengesdownstream:inreview,testing,release,andgovernance.Thequestionfacingmany

leadersisnolongerwhetherAIimprovesproductivity,buthowtomeasureandsustainthatimprovementacross

complexsystemsofpeople,tools,andprocesses.

AtLinearB,weviewAIProductivityasthenextevolution

ofSoftwareEngineeringIntelligence.Measuringtraditional

outputssuchasCycleTimeorPRThroughputremainsessential,butleadersmustnowalsounderstandhowAIinluencesthesemetrics,andhowitreshapesthesystems,processes,and

behaviorsthatunderpinthem.AssessingAIproductivitynowrequiresbothquantitativeandqualitativeinsight–capturingnotjustwhat’schanginginperformance,buthowteams

areexperiencingandadaptingtothosechanges.

2026BENCHMARKSREPORT4

That’swhyfor2026,wehaveintroducedanewqualitativedimensiontocomplementourquantitativedata.Inadditiontothemetricspulledfromacrossthesoftwaredevelopmentlifecycle,thisyear’sreportincludesinsightsfromour2026

AIinEngineeringLeadershipSurvey,astudycapturingthe

perspectivesofengineeringexecutives,platformleaders,and

DevExprofessionalsontheorganizationalimpactofAI.Their

experiencesandobservationsbringthedatatolife,capturingreal-worldperspectivesfromthedevelopmentcommunityonhow

AIistransformingproductivity,codequality,andDevEx.

Wehopethatyouenjoythesefascinatingnewinsights

intohowAIisreshapingthecraftandcultureofsoftwareengineering.Aboveall,the2026SoftwareEngineering

BenchmarksReportisdesignedtohelpyouunderstandnotonlywhereyourteamsstand,butwhy–offeringbothdataandperspectivetoguidesmarterdecisionsintheAIera.

YishaiBeeri[CTO,LINEARB]

[INTHISREPORT]

You’llindallmetricsorganizedbythefollowing

criteria:1)AllOrganizations,2)bySizeband,3)

byGeography.It’simportanttonotethatalldatahasbeenanonymizedandnormalized.Foraggregation,weusedtheP75(75thpercentile)calculation.

P75islesssensitivetoextremevaluesoroutliersinthedata,providingarobustandreliablemeasure.

[TOOLTIP]

WE’VEORGANIZEDTHEDATAINTOTHEFOLLOWINGLEVELSOFPERFORMANCEFOREACHMETRIC:

Elite

Top10%theincludedOrganizations

Good

Top30%theincludedOrganizations

Fair

Top60%theincludedOrganizations

Needsfocus

Bottom40%theincludedOrganizations

2026BENCHMARKSREPORT5

SoftwareengineeringbenchmarksBENCHMARKSBYORG|8,109,244PULLREQUESTS|

4,813TEAMS|163,820ACTIVECONTRIBUTORS

Category

Metric

Elite

Good

Fair

Needsfocus

Delivery

CodingTime(hours)

<54mins

54mins-4hours

5-23

>23

PickupTime(hours)

<1

1-4

5-16

>16

ApproveTime(hours)

<10

10-22

23-42

>42

MergeTime(hours)

<1

1-3

4-16

>16

ReviewTime(hours)

<3

3-14

15-24

>24

DeployTime(hours)

<16

16-106

107-277

>277

CycleTime(hours)

<25

25-72

73-161

>161

MergeFrequency(perdev/week)

>2.0

2-1.2

1.2-0.66

<0.66

DeployFrequency(perservice)

>1.2

1.2-0.5

0.5-0.2

<0.2

PRSize(codechanges)

<100

100-155

156-228

>228

PRMaturity(%)

>89%

89-83%

82-77%

<77%

Predictability

ChangeFailureRate(%)

<1%

1-4%

5-17%

>17%

RefactorRate(%)

<11%

11-16%

17-22%

>22%

ReworkRate(%)

<3%

3-5%

6-8%

>8%

CapacityAccuracy(%)

85-115%

75-85%or115-125%

70-75%or125-130%

>70%or>130%

PlanningAccuracy(%)

>82%

82%-64%

63%-47%

<47%

Project

Management

IssuesLinkedtoParents(%)

>90%

90-67%

66-56%

<56%

BranchesLinkedtoIssues(%)

>77%

77-62%

61-41%

<41%

InProgressIssueswithEstimation(%)

>55%

55-26%

25-14%

<14%

InProgressIssueswithAssignees(%)

>96%

96-84%

83-76%

<76%

2026BENCHMARKSREPORT6

ENTERPRISE[1000+EMPLOYEES]

Softwareengineeringbenchmarks

CategoryMetric

Elite

Good

Fair

Needsfocus

Delivery

CodingTime(hours)

<2

2-6

7-26

>26

PickupTime(hours)

<2

2-4

5-13

>13

ApproveTime(hours)

<15

15-23

24-45

>45

MergeTime(hours)

<2

2-4

5-16

>16

ReviewTime(hours)

<3

3-13

14-23

>23

DeployTime(hours)

<25

25-173

174-347

>347

CycleTime(hours)

<27

27-89

90-165

>165

MergeFrequency(perdev/week)

>1.4

1.4-0.9

0.8-0.5

<0.5

DeployFrequency(perservice)

>0.7

0.7-0.3

0.2-0.1

<0.1

PRSize(codechanges)

<100

100-141

142-220

>220

PRMaturity(%)

>89%

89-85%

84-78%

<78%

Predictability

ChangeFailureRate(%)

<1%

1-2%

3-26%

>26%

RefactorRate(%)

<11%

11-17%

18-23%

>23%

ReworkRate(%)

<3%

3-5%

6-8%

>8%

CapacityAccuracy(%)

85-115%

75-85%or115-125%

70-75%or125-130%

>70%or>130%

PlanningAccuracy(%)

>82%

82%-64%

63%-47%

<47%

BENCHMARKSBYORG|8,109,244PULLREQUESTS|4,813TEAMS|163,820ACTIVECONTRIBUTORS

2026BENCHMARKSREPORT7

SCALE-UP[200-1000EMPLOYEES]

Softwareengineeringbenchmarks

CategoryMetric

Elite

Good

Fair

Needsfocus

Delivery

CodingTime(hours)

<1

1-6

7-31

>31

PickupTime(hours)

<2

2-6

7-16

>16

ApproveTime(hours)

<17

17-24

25-46

>46

MergeTime(hours)

<2

2-5

6-19

>19

ReviewTime(hours)

<5

5-18

19-26

>26

DeployTime(hours)

<17

17-124

125-296

>296

CycleTime(hours)

<45

45-95

96-169

>169

MergeFrequency(perdev/week)

>1.6

1.6-1.1

1-0.6

<0.6

DeployFrequency(perservice)

>0.9

0.9-0.5

0.4-0.2

<0.2

PRSize(codechanges)

<103

103-157

158-212

>212

PRMaturity(%)

>88%

88-82%

81-76%

<76%

Predictability

ChangeFailureRate(%)

<1%

1-3%

4-13%

>13%

RefactorRate(%)

<12%

12-16%

17-22%

>22%

ReworkRate(%)

<4%

4-5%

6-7%

>7%

CapacityAccuracy(%)

85-115%

75-85%or115-125%

70-75%or125-130%

>70%or>130%

PlanningAccuracy(%)

>82%

82%-64%

63%-47%

<47%

BENCHMARKSBYORG|8,109,244PULLREQUESTS|4,813TEAMS|163,820ACTIVECONTRIBUTORS

2026BENCHMARKSREPORT8

START-UP[0-200EMPLOYEES]

Softwareengineeringbenchmarks

CategoryMetric

Elite

Good

Fair

Needsfocus

Delivery

CodingTime(hours)

<1

1-4

5-23

>23

PickupTime(hours)

<2

2-5

6-17

>17

ApproveTime(hours)

<8

8-22

23-41

>41

MergeTime(hours)

<2

2-3

4-17

>17

ReviewTime(hours)

<3

3-13

14-24

>24

DeployTime(hours)

<15

15-94

95-268

>268

CycleTime(hours)

<25

25-71

72-164

>164

MergeFrequency(perdev/week)

>2.2

2.2-1.4

1.3-0.8

<0.8

DeployFrequency(perservice)

>1.4

1.4-0.6

0.5-0.3

<0.3

PRSize(codechanges)

<105

105-158

159-235

>235

PRMaturity(%)

>89%

89-83%

82-77%

<77%

Predictability

ChangeFailureRate(%)

1%

1-4%

5-16%

>16%

RefactorRate(%)

<11%

11-16%

17-21%

>21%

ReworkRate(%)

<3%

3-5%

6-7%

>7%

CapacityAccuracy(%)

85-115%

75-85%or115-125%

70-75%or125-130%

>70%or>130%

PlanningAccuracy(%)

>82%

82%-64%

63%-47%

<47%

BENCHMARKSBYORG|8,109,244PULLREQUESTS|4,813TEAMS|163,820ACTIVECONTRIBUTORS

2026BENCHMARKSREPORT9

Metricsdefinitions

DELIVERY

CodingTime

Thetimeittakesfromtheirstcommituntilapullrequestispublished.ShortCodingTimecorrelatestolowWIP,smallPRSize,andclearrequirements.

ReviewTime

Thetimeittakestocomplete

acodereviewandgetapull

requestmerged.LowReview

Timerepresentsstrongteamworkandahealthyreviewprocess.

DeployFrequency

Ameasurementofhowoftencodeisreleased.EliteDeployFrequencyrepresentsastableandhealthy

continuousdeliverypipeline.

PickupTime

Thetimeapullrequest

waitsforsomeonetostart

reviewingit.LowPickupTimerepresentsstrongteamworkandahealthyreviewprocess.

DeployTime

Thetimefromwhenabranch

ismergedtowhenthecode

isreleased.LowDeployTime

correlatestohighDeployFrequency.

PRSize

Thenumberofcodelinesmodiiedinapullrequest.Smallerpullrequestsareeasiertoreview,safertomergeandcorrelatetoalowerCycleTime.

ApproveTime

Thetimefromirstcomment

totheirstapproval.Thismetric,alongwithMergeTime,isamoregranularsegmentofReviewTime.

CycleTime

Thetimeittakesforasingle

engineeringtasktogothrough

thedifferentphasesofthedeliveryprocessfrom‘code’to‘production’.

PRMaturity

Theratiobetweenthetotal

changesaddedtoaPRbranchafterthePRwaspublishedandthetotalchangesinthePR.

MergeTime

Thetimefromtheirstapprovaltomerge.Thismetric,alongwithApproveTime,isamoregranularsegmentofReviewTime.

MergeFrequency

Thetotalnumberofpullrequestsormergerequestsmerged

byateamoveraperiodoftime.

2026BENCHMARKSREPORT10

Metricsdefinitions

PREDICTABILITY

ChangeFailureRate(CFR)

Thepercentageofdeploys

causingafailureinproduction.

ReworkRate

Theamountofchangesmadetocodethatislessthan21daysold.HighReworkRatessignalcodechurnandisaleading

indicatorofqualityissues.

RefactorRate

Refactoredworkrepresentschangestolegacycode.LinearBconsiders

code“legacy”ifithasbeeninyourcodebaseforover21days.

PlanningAccuracy

Theratioofplannedworkvs.whatisactuallydeliveredduringasprintoriteration.HighPlanningAccuracysignalsahighlevelofpredictabilityandstableexecution.

CapacityAccuracy

Measuresallcompleted

(plannedandunplanned)workasaratioofplannedwork.

PROJECTMANAGEMENT

IssuesLinkedtoParents

Thepercentageofissuesor

ticketswithinyourPMinstancethatarelinkedtoaparentissue,suchasanepicorstory.This

doesnotincludesubtasks.

BranchesLinkedtoIssues

ThepercentageofcodebranchesthatcontainareferencetospeciicPMissues,providingvisibility

intothealignmentofcodechangeswithplannedtasks.

InProgressIssueswithEstimation

TheproportionofongoingPMtasksthathavetimeoreffortestimatesassigned.

InProgressIssueswithAssignees

ThepercentageofactivePMtasksthathaveadesignatedteammemberresponsibleforcompletingthem.

2026BENCHMARKSREPORT11

Metricsdefinitions

AI

AgenticAIPRs

PullrequestscreatedbyAIagents,runningadvancedagenticlows(e.g.agentsthataregivenahigh-level

task),andperformafullprocessforgeneratingthecode,committingit,andcreatingapullrequesttogetitmerged.SometimesthesePRscanrepresentasmallertasksuchas

ixingasimplebug,typoetc.

ExamplesofsuchagentsincludeDevin,CopilotCodingAgent,

OpenAICodex,andothers.

AcceptanceRate

ThepercentageofPRsthatget

mergedwithinX=30daysofbeingcreated(ormovedoutofdraft).

Note:Wehavestandardizedona

mergethresholdof30days,butourinsightsholdforthresholdsof7d,

14d,andevennotimelimitformerge.

2026BENCHMARKSREPORT12

Newthisyear:AcceptanceRatebenchmarks

Thisyear,we’reexcitedtoaddanewbenchmarktoourstudy:

AcceptanceRate

AcceptanceRateisthepercentageofPRsthatgetmergedwithinX=30daysofbeingcreated(ormovedoutofdraft).

TheAcceptanceRatemetricandbenchmarkareparticularly

relevanttoAI-generatedcode,whereownershippatternsand

acceptancelowsarebeingchallenged.Andindeed,early

AcceptanceRatebenchmarksrevealthatAI-generatedPRs

operateinacompletelydifferentperformancerangethanmanualones,underscoringhownewanduneventhisworklowstillis.

ACCEPTANCERATEBENCHMARKS

ELITE

GOOD

FAIR

NEEDSFOCUS

AllPRs

>95%

91-95%

85-90%

<85%

ManualPRs

>95%

92-95%

87-91%

<87%

AgenticAIPRs

>71%

61-71%

42-60%

<42%

2026BENCHMARKSREPORT13

WhileteamsqualifyasEliteonlywhentheyexceeda95%AcceptanceRateformanualPRs,AIPRsreachthesame

QUALITATIVEINSIGHTSAIINLEADERSHIPSURVEY

tieratjustabove71%,adramaticallylowerbarthatrelectsthecurrentrealityofhowteamshandleAIcontributions.

FORAIPRS,MOST

TEAMSFINDIT

HARDTOEXCEED

ACCEPTANCERATEOF

60%

Thisdividecontinuesacrossallbenchmarktiers,withmost

teamsindingithardtoexceedanAcceptanceRateof60%for

AIPRs–farlowerthanobservedformanualPRs.Thishintsat

thefundamentalchallengewithAIintheSDLC:creatingmore

codefasterdoesnottranslatetohigherdelivery.Reviewers

approachAIworkwithgreaterscrutiny(seequalitativeinsights

below);AIoutputstilllackstheconsistencyandtrustlevelsof

manualcode;andunclearownershipleavesmanyPRsabandoned.Asaresult,AIPRsarenotjustunderperformingrelativetomanualPRs;theyhavecreatedanewbenchmarkrangethatshowsmorecodedoesnotnecessarilyresultinincreaseddelivery.

Whatengineeringleadersaresaying

Oursurveyresponsesmakeitclearthatcontext(not

justcorrectness)isthesinglebiggestbarriertomerging

AI-generatedPRs.LeaderstoldusthatAIoftenproduces

changesthatlookreasonableonthesurfacebutlackthe

deeperrationale,intent,orsituationalawarenessreviewersneedtofeelconidentapprovingthem.Withoutthatcontext,evenclean-lookingcodecanfeelrisky:reviewersworry

abouthiddensideeffects,missingassumptions,orlogicthatdoesn’talignwithhowthesystemactuallyworks.

Asaresult,theyslowdown,askmorequestions,orrejectthePRentirely–notbecausethecodeiswrong,but

becausetheycan’ttrustwhattheycan’tfullyunderstand.

Here’showtheyputit:

“AIgeneratesadvancedcodetheauthorcouldnotexplain.”“Contextisoftenwrongormissing.”

“SomeofthecodeAIgeneratesisverylargeandmaynot[be]neededforthecontext…”

“Withoutcontextitgeneratesmoreworkthanajuniordeveloper.Theworkloadincreasesforthereviewer.”

AIproductivityinsights

AICODEINTHESDLC

15

THEDEVEXOFAI

26

THESTATEOFAIREADINESS

33

[DISCLAIMER]

IT’SIMPORTANTTONOTETHATCORRELATIONDOESNOTINDICATECAUSATION.HOWEVER,THEINSIGHTSTHISDATAEXPOSESALIGNCLOSELYWITHTHEQUALITATIVEANDANECDOTALRESEARCH

WE’VEGATHEREDFROMLINEARBUSERSOVERTHEPASTYEAR.

AIcodein

theSDLC

[INSIGHT

[INSIGHT

[INSIGHT

[INSIGHT

[INSIGHT

[INSIGHT

01]

02]

03]

04]

05]

06]

AI-AssistedPRs

are2.6xlargerthanUnassistedones.

AIPRshaveaPRPickupTime5.3xlongerthanUnassistedones.

Acceptance(Merge)

rateforAIPRsislessthanhalfthatofmanualPRs.

HeavierAgenticAI

adoptionisnottranslatingtogreatervaluedelivery.

AIPR30-day

Acceptance(Merge)

Ratevarieswidelybytool.

AIuseisbiased

towardsnewcode,

ratherthanrefactoring.

AICodeintheSDLC

Inthissection,we’llexaminebenchmarksacrossthreetypesofpullrequests:AgenticAIPRs,AI-Assisted

PRs,andUnassistedPRs(asdeinedbelow).

[PULLREQUESTTYPES]

AgenticAIPRs

WedeineAgenticAIPRsaspullrequestscreatedbyAIagents,

suchasDevin,CopilotCodingAgent,OpenAICodex,andothers.Theyrepresentadvancedagenticlows,typicallyagentsthataregivenatask(directly,orviaaJiraticket,forexample),andperformafullprocessforgeneratingthecode,committingitandcreatingapullrequesttogetitmerged.SometimesthesePRscan

representasmallertasksuchasixingasimplebug,typoetc.

Earlierexamplesof(non-AI)botPRsinclude

dependencymanagementPRscreatedbyDependabot,RenovateandSnyk,forexample,tobumpversions

ofdependencylibstopatchsecurityissues.

AI-AssistedPRs

DeinedasacodechangeauthoredorsigniicantlyshapedwiththehelpofAI-powereddevelopmenttools.ThesePRsoriginate

fromhumanintentandownership,withtheAI

contributingsuggestions,codeblocks,orrefactorsthatthedeveloperintegratesintotheinalchange.

AI-assistedPRsdifferfromagenticlowsinthattheAIdoes

notindependentlyinterprettasks,commitcode,oropenthePR.Instead,thedeveloperremainsincontroloftheworklow,usingAIasanaugmentingtoolratherthananautonomousauthor.

UnassistedPRs

PullrequestsauthoredentirelybydeveloperswithoutsubstantialuseofAItoolsforcodegeneration,modiication,orrefactoring.

AsagenticAIsystemstakeonmoreoftheend-to-end

developmentlow(interpretingtasks,generatingcode,and

openingPRswithouthumanauthorship,fore.g.),theirimpact

onengineeringworklowsbecomesincreasinglyvisible.While

adoptionissurgingandAIhasbecomeadailytoolformost

developers,thetechnologyitselfisstillearlyinitsmaturity,andthatshowsinthePRsitproduces.PRswithAIinvolvement(bothAgenticAIPRsandAI-AssistedPRs)oftenarrivelarger,broaderinscope,andlesspredictablethanmanualchanges,placingnewdemandsonreviewerswhomustinterpretintentandassessrisk

2026BENCHMARKSREPORT16

2026BENCHMARKSREPORT17

withoutthebeneitofhumancontext.Theresultisagrowing

mismatchbetweenwidespreadAIusageandtheworklows

builttosupportit:AIhelpsdeveloperswritecodefaster,but

reviewersarebeingaskedtoshouldermorecomplexitywith

toolsthatarestillevolving.Atthismoment,AIisbothacceleratingcodeoutputandmakingthereviewprocessmorecomplex,

highlightingjusthownewthetechnologystillis.

[INSIGHT01]

AI-AssistedPRsare2.6xlargerthanUnassistedones.

MEDIAN

SIZE

P75SIZE

AgenticAIPRs

89

293

AI-AssistedPRs

96

408

UnassistedPRs

26

157

AI-generatedPRstendtocomeinmuchbiggerthantheones

writtenbyhumans,andthatalonechangesthereviewexperience.Atthep75mark,AgenticAIPRsreach293linesofcodecomparedto157linesforUnassistedPRs,whichmeansreviewerssuddenlyhavemuchmoretoprocess.Largerdiffsoftentouchmoreiles

andmorepartsofthesystem,raisingtheoddsofdownstream

issuesandcreatinghigherlevelsofcodecomplexity.Andbecause

408

89

293

96

157

26

LINESOFCODE

450

400

350

300

250

200

150

100

50

0

P75SIZE

MEDIANSIZE

AI-ASSISTED

PRS

UNASSISTED

PRS

AGENTIC

AIPRS

2026BENCHMARKSREPORT18

there’sjustmorecodetounderstand,reviewershavetospendextratimeiguringouthoweverythingitstogetherbefore

theycaneventhinkaboutsuggestingchanges.Additionally,

AI-AssistedPRsarenowthelargestofallthreegroups,hitting408linesatP75,suggestingthatwhendevelopersrelyonAItogenerateorreinecode,thetotalvolumeofchangesstillgrowsquickly,evenwhenthedeveloperretainsauthorshipandintent.Allofthisaddsuptoareviewprocessthatfeelsheavierfromthestart,evenwhenthecodeitselfmightbeperfectlyvalid.

Assistedvs.Unassistedcommits:Fileandlinecountcomparison

ASSISTEDCOMMITS

UNASSISTEDCOMMITS

Averagenumber

ofilesmodiied

4.2

7.51

Averagecodelines

273

498

Averagecodelinesperile

65

66

DivinginsidethePRsandintotheseparatecommits,weseeadifferentpattern.AI-assistedcommitstendtobemore

concentrated,touchingfewerilesonaverage(4.2,compared

to7.51fornon-assistedcommits),whilemaintainingasimilar

averagesizeperile(65vs.66lines).Thisseemstoindicate

thatAItools,whichcreatecommitsfordevelopers,aremore

“disciplined”withregardtocommitboundaries,whereashumanstendtoaccumulatemoreworkintoeachcommit.Takentogether,theseaveragesshowthatbothAgenticAIandAI-AssistedPRsconsistentlyintroducelargercodechangesthanUnassisted

PRs,amplifyingthevolumereviewersmustevaluatewhileshiftingtheshapeanddistributionoftheunderlyingwork.

QUALITATIVEINSIGHTS

Whatengineeringleadersaresaying

Whenweaskedengineeringleaders,“What’sbeenthe

biggestchallengeorconcernwithusingAIinyourrole?”*,anoticeablesubsetofresponsespointeddirectlyto

thegrowingsizeandscopeofAI-generatedchanges.

Here’showtheyputit:

“Verbosecodegenerationin

distributedproductionsystems.”

2026BENCHMARKSREPORT19

“AItoolstendtomakechangeslargerthantherequest’sscope.”

“Beingabletofullytrusttheresults.GettingWAY

moretext/informationbackthanyouwantedor

needed,whichslowsyoudownbecauseyouhave

tohuntforwhatyouactuallywereinterestedin(cansomewhatbedealtwithbywritingbetterprompts,butitisstillprettycommonevenwithgreatprompts).”

[INSIGHT02]

AgenticAIPRshaveaPRPickupTime

5.3xlongerthanUnassistedones.

PICKUPTIME

P75

REVIEWTIME

P75

AgenticAIPRs

1055

382

AI-AssistedPRs

496

194

UnassistedPRs

201

252

PICKUPANDREVIEW

1500

1400

1300

1200

1100

1000

900

800

700

600

500

400

300

200

100

0

382

PICKUPTIMEP75

REVIEWTIMEP75

194

1055

252

496

201

AI-ASSISTED

PRS

UNASSISTED

PRS

AGENTIC

AIPRS

2026BENCHMARKSREPORT20

AI-generatedPRsexperienceanimbalanceinthereview

process:theywaitsigniicantlylongertobepickedup,yet

oncesomeonestartsreviewingthem,thereviewitselfisonly

somewhatslowercomparedtoUnassistedPRs.AttheP75

mark,AgenticAIPRssitidlefo