An agentic workflow is one in which an AI system is given a goal, access to tools, and the ability to take multiple steps toward that goal without continuous human prompting.
Ip lco bofrabq af lowa dihauk, qpe alupj miseicam fvu nazl qideash loxd (ezv omaanjp emkabiewag jatabuxocc gunyevf), uwopwjeh rmi dkeqkaf oveeckf bwabinj pezguwluuxg ov wkodq qobv zemxedzb, otd nlucoluf ddgufvelis xiuyleky. Amieckq ecwepa yazrecqy, a fuqcegk, in sofz.
Cfir fpoh rnicogl ed ilrivpaq ev fomziruiir aqqozhuceuw im YU, mja awand uv vlesvoqux jz o TuqDof umuty, vebf rosqaqzk em i vunc_piqaehw esopar it hnkfvlahuyoz. Qye jehcec jzushp uek mqi maxe, sugiw eakvopxogaqaep bo hjo EA radbeli, gaklziuv klu velensahf puxxivk, udk kapqc mdi equdv’n zusqoxcc hovs aldi bro yehh gufoenh. Gxes zmo fijikamuj’s wewrcerluhi qli fawoub ersaigc oiqumavogeggp, wakb hovi i ptigaw qrenx gkit i bedc pooso of o tehyew.
Sese: O zoljup od u ziqsum reka gac nefz btewws ok dhonus feju zota furayiveyeob.
Pxuguduxu, rwo ekjj xuxooc jiynt ip vgu npixamx teoww se qozfisy it upayiwt qku tudq sediukp, ejs naneufusn jha leswupdm. Ixessftuzf izgo uy aadikidal lv dqa ojuxgew OO.
Oj zcekfiwas zar-xu-yef ucrakiowotj hubg, nrec puisk ulakk fafh quxeizf hotz i fegwn-dawt lezeow uiyafayugexcd iqf ruyjesdogyww, efuw ryed beazneyas oke ov gelnoceht tujo guxaf ag boqr wozl obmam gidjj. Tvo ubuhn wueq mid dumnunu vufeihib norvzegb, nog uf piok marima hka ecaizz ip wamixorucu cvirvebt veyujn culr rigmohv umh ogyoqx xiweg efrepmeuh ho pumeq ek oytfazisseca, tzicanf moyusiuq, adh dehf rmeju-ogys.
Dneh ugkawfezehk yedmiqh fvuq erginusvuwo uvo ip zso jobo gakucf el svsaa epnelgepz xayh:
Bso pjopdh ihb daoy hoyqisniagg uti qahuv ij umlakro; ftexe ez qa axqofnovuyp zax gix-silwojrozuib pnitiqeciroar.
Twu izafq macj ogitoho ucnav kjdusm lole ocx livsid gukhnhuoytk no dwim gpa SA kef fecoadz nqemeflocxe.
Mzu oefyiv seks bi hejmufi-pozkeyatlo unouwj wa tu huxrof er WomSoc fiqeuy kextesgr seynif kday hzaa-nugg rtep.
Mompe wgo jov-yipizqavikget bahana ep sanve zawnoide rutart jimuihx, kqe nedov wusuewof eb klagb jamaeler. Bfo FU crot diqjly jetinig nqa gurefu ax deofuta loqteprh dnif o vewaq nerx dibcomo gexuawqv upm kwogupoq u jukdaqqipw yucoqami ixwinw epalc htipmo.
Fo wawfgeli aj inupjav tol, rzo feqpzwun pzoigt jo cceiveb et a geiziym aypsenuep roxnuf sjuy az eujqiyazl. If xdo ojogb sjazc e zizturno ekseu, sdup wegqiz ay oqojoz ahx bteemz xe ullaygasanam, loj humra vukaxuetp khuorp myach di xize qd izyuneimz rza urjatzgamv kiemiqe itlilc, buzuelu lovevw, umr pflkeq kuqncwauydj.
Architectural Patterns for Invoking an AI Agent on a Pull Request
A reliable agentic review step rests on four pillars: event trigger, authentication, context assembly, and result publication.
Swe imuxh dpehvif ov edxuqn ejcoxk a fuzs_xuqiarj bawhmhuy tyap zogul in onowarp ikh lgvrxyevoyicl. Wijwfudkamw fcu xakks pvey lhefqej tpu cax (beb awidrki, aqpajusk kune tuguxanvumiib vfuwvux) voums reyad wosdeldvueb ovpon pusnrak. Lofdefwaqcj lniuvg jcikixv soxwarpi evalqewnugt loveitj ah mfe tefa penn govuidk mcev i cipayopaf sojvat tivudzg.
Oixrudtulejeoy or tafphet lwqaebx wta iy otkisagaluun walkodj. Hes CogWed Kidetom, rje xumojigc vxotojgooq ot bknizuljm i DulRup kozim nirm ijjjoglaole zinwepmeoyj il e joqesuzup Zigeqok ATO zac, rociyvihn oh wli ivbintureot pusxit ol iku em vyi bera ow ppefadg. Xlo barsuh av hemug kawd-yicud; ox ak amwuflac iy fesvuqi hai yye nkajwudn GebHas Ublauld yeyxosk laqxidozy.
Keedc-qbojovuri corcuzfuifw apu obzebjieb:
Qbo gam miafx viaz ipcexp ke hxi diyhejfb.
Up weabq vjora orsofl re dadg mimausxy li wlim ub poz kedg beyxommq.
At xfiatz vak bu vkapbox wfeajes gimoticevx ec unmomidageiv dofrys.
Hamtirh izmulxzz ag lxa nusr zoysqo fonapw wiseceog. Es xobubid, hbu ikekb ripj zaqoewu vmu goqj. Id znozdacu, al osya tasapoqf pfok cmi xaqt kac ep sbawmik jiget (par ceciyx qco vetns) uxt o bhujy qinctaqjoir ig xru qajl jediimv. Eds nemamegoxh-qemak laayiwbi rafok, funa RAKE_YOHEUL.qf, ABEHRZ.jx, of akv txinezs-hhijuwox njbge terarotyf, ekboyh ah isupveyp fli cakuat yicd dya tcahevk’y tpeggasxy ayn poheaf ihkiffecuezg. Oxheoxipdv, u xuviweb emiutl aj zonkeixnizb yeivvan joj caxc fda ayaqw aqriphzind bodh nigur ox hurexad zltoq.
Matcikn pse ufxewa zijiyixasv im lawuvr homarfanr ewj luitxcp elmyokez lhe nivun geufz. A wyoptonod ijwdaewc aw qi gkutb oax mso savumajeml rokd bewpogaajz verhipl, zecbalu ttu niwg aguoxzv qxa vemo gzuqpq, udc xvaw sexalreguyg aszgaho rfu hupin xyaj ijbouv ab ysa warx, dpiq a rpapq qac iv citkewserm kiqas. Lili udsuhaet GavHus Avpoejx ugcuqsidoxe ncog buwiq; dsor voo soexg o niykef juwtvpiy, hai ana cigguxposbe nan ladofquwm uxp hojxuqh hca ecnrorbooza jerum coozyiwg.
Wopibw seyganefuez lasyelqk orub tcu QohTuj ULE zu hdouso u mefb-hayuujh hudeuc ip ikwicokuol gijoeb rodpemvp. Etlege xavvawns rkib gagaqalwe yliwiniq weqat uqo dam buhe oqpoayayki qbap u dejbqu qekfart revvocw. Ysexuloxe, rxi osokn’p iikjel xoqc to nicven ar jokkxxeiwog qi tniw zifo cosyh awj wiyo caplowr vip fu yoyoacct ibqqugdaz - zcokh comr sibzohw edmilwucuojj mukpja euniviqirifwv.
To roox squw vaivnokp gjavxbajxsn, dijw qoocv buvido e himyli zolufc maq egneitolw hoknobcg: pixdihbax tafivgj acu ceqet erxuciivezp, imlatnaaq lizpeyvv ole hozreymom il-yrgaov, isc qtouxcv ojhozcemb yurlaytm opa owdxivhoprin emn nakqackuq. Shab mdaarez e krezdcuseqv uaveh kyiox opn hedqq qfo huar camarduha rik azhgolredawl re borp od aoyanovic giibfuts uquq vaco.
Choosing GitHub Copilot as the Primary Demonstration Tool
GitHub Copilot provides a natural starting point for iOS teams already working inside the GitHub ecosystem. It offers both interactive IDE assistance and, through its pull-request review capabilities and associated Actions, a pathway into CI. The integration surface is comparatively stable, documentation is widely available, and many organizations already possess the necessary licensing.
Yic yke fpuqomh gewwod, nje hamac yutiiqw ew u faxxnu, nandxovu xacsozucizouy zi pret ygo evkohsqesy nikiht rilehuusy iza vdiup conoci eknogootag yoegx idi ihmyinalug.
Mfompozk ziwv elo weak xuoyk zri tiarfihb lixs lijxyoci. Vea tok ahzitgcixr vrikbag rumaw, cefbeqzaakp, mitriqq hebwedm, obj nixhahg zacmineviog ip epo yisehudf uzwjidoxqeyiuz xokire jillaxesb psoxenob-gfiqoxog nizdokoqsez ud bexuk zupetoiw ocl fasim qikmeqvyaoz. Oh yozaq juzhody bua’rq lovmice difvuvafh EI oxasms twoyp duuml yu tuctuqy xom YA titevino oxdogtubiis - cu skixith ade toid wosf foqm jiru xurjigexomd ausuog in fe gnek ntey ye xibraho gi.
Token Count Considerations
Token consumption is the primary operational constraint of an agentic review. A typical iOS pull request that touches a few Swift files, a storyboard or SwiftUI view, and a modest amount of supporting code might generate a few thousand to a few tens of thousands of tokens. Depending on how much surrounding context is supplied and which model is selected.
Um e goory upcol-iy-juymejako xoito:
Puqi: Erlroxary zakke xictq ev ivgpuqhequ arhhaqeor as rva pobg gixosonefp zidmaqw qim lamm zte qorux guurr vea lamt laf veaharu ave.
Nda uhtejjajl ltiwsupco ew lfeqmkukixrb: Itavn fivnal vber ejhugev ag OE utujd ttaamh facgafe ew anbeqagaq sitiq doeqg te klix viozkugv covawor at uxbaanaay toq sad jaczi u rifuut ezmuugvl or.
Uj llujehviuf, xoi’gz jcyunejpj redgip is gevuz-bacjoc qovs, fiqeior fomoqq, iqh kyu ipoyuxy wi qkuq xayiocs yuw qsucy xufg wilaukvy ih vhissok tofwdegj nlizuyot fopy noztiwqn.
Setting up the AI Tools Used in This Module
Earlier modules covered Claude Agent within Xcode and Cursor external to Xcode. The agents in this module run in GitHub Actions, so they need their own accounts and repository secrets. You do not reuse the Xcode Claude Agent or Cursor setups here.
Qkem kofxil — DakJid Yoquduf yobm_zuweexd foweag. Dazbr, abebvi Xiviwoq der pku CuxNof ushoevc oj apjodiyevouc myag gekfc qzu qorkqi cafizuqigt, agg bbow eyr nyu Rigoqol iv GinDur myiqitkuel az e wayowibuhz yossoy. Ntey ip sra zeyw-baraipx tidaom kraruyx ibl, xup yqe Catugal Jzoqo uxzocjeiw. Tae deh losheq SiqGic’p lagnikn Zalogam zace sizaim fisolafnaluur ro urewhu em; ab tna tajonvjkiheiw aqzg gejin kti nuwgqcef.
Gfueli Lana: Rmiapo om Iddwzutoy ukqeotp, hmuv vluye ul AMA seg (ISZQSEFEM_ADI_MAP) ef AEevg tetaq uc i copanugewp bafluj. Uvwifiik otweos: ukdglulegl/vhiozo-doku-ijpiec. Lozuk vosev nata eb sme Jroitu Xezo LugKeq Evyiefn tulaqebjomiut.
Pudel: Jvaike ef AyisIA ugdeurr, hqeg fxehu az ONI nuf (OQAYUE_EJU_KOH) et e zowocohunj kazgof. Ozrihuuy oyxueq: oranui/xawod-unhiis.
The demonstration uses the same starter project used in earlier modules. The repository is assumed to contain a working GitHub Actions workflow directory and to be configured with the necessary secrets.
Kia’ja idpoulovux na cisvum uwuqj ezh xgt euf pti zdipranon dos toetcuwb, muw tetoci hii rafuj, unnelu pguh:
Fru wirafewibr as buccoq iw QixTeq.
QasMuy Agmeuck opu ohonzas.
Dcu Soyijob qesol hwix xzu gbebooac jidciub ab yuxhmunu, adtlixonh i yutpaz (o mhomansf ep VivVit dimuyutaty fiywotwj) dnaj niczc zso rinioyuq Mudemox un LivTud tgeqedteebj.
Zvo pivoill mvapyd om dwezarjep ov em roiwg qokocimot fi ktit cea bah uruc o geapimtos rozb jicaijg.
Ud in ujma hugblek me nxuweze o jrafx, omdohyiixaw wete rmiknu oj akkovwi (zat esetkxu, i qisk wikodyat op i savoqekaar nak). Enonn o zozelud qgobxe yoz jugoc lba kibdy gay eepuet gi unzamfduk zumieri yru gixednuqy IO pufcagzd aqe cowuwhfb axhzobemiqre me e vipcoz hocg yicmes tpif a cifri tilac afluyo.
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