I-AI ikhiqiza ikhodi enhle kakhulu. Kodwa lokhu kungcono: ukucacisa inkalo yokukhiqiza. Ukukhiqiza kunezinto. I-Code ibonisa ukuthi kufanele kube; izixhobo zokubuyisa zibonisa ama-signals; izinhlelo zokubuyisa zibonisa ama-problems; I-CI / i-CD ibonise izinguquko. Yonke indawo ibonise isikhwama sokukhiqiza. Akukho akuyona imodeli enhle yokusebenza kwe-system ngokuvamile. I-SRE, i-Support, i-QA, i-Dev, i-PM. Akukho ukulungiselela okuzenzakalelayo ukuthi isofthiwe yokukhiqiza isebenza kanjani. Ngaphandle kokusebenza kwama-team, izilimi ze-knowledge ze-individual zibonisa imibuzo kumazwe. Ukuphathelela ukukhiqizwa kuyinto ephilayo nokuhlanganiswa - ibheka ku-code, ibhokisi, amabhokisi, ulwazi se-tribal, kanye namasiko eminyakeni amancane. Ngakho-ke lapho kube isikhathi sokuphendula inkinga le-customer ekukhiqizeni, imibuzo ibonise inkinga: ibheka okuhlobene, okuhlobene, kanye ne-siloed. Ukuze i-AI iyasiza ngokwenene, kufanele ufunde "ngokuthi", emkhakheni izixazululo eziyinhloko. Akukho kuphela lapho thina namhlanje, kodwa kanjani siphinde apha. I-Two Clock I-Problem Kuyinto ingxubevange phakathi kwesimo se-akhawunti yamanje kanye nesimo se-historical ibizwa ngokuthi "isimo se-two clock." Lapha isibonelo esiza kusiza ukwandisa umlomo wakho: I-CRM yakho ibhekwa i-value ye-deal ekupheleni, akuyona i-negotiation. I-system yakho ye-ticket ibhekwa "ukuguqulwa", akuyona i-motivation. I-codebase yakho ibhekwa isimo se-akhawunti, akuyona ama-debates ezimbili ze-architectural okwenza lokhu. Thina wahlala isakhiwo se-trillion-dollar ngenxa ye-truth now. Hhayi. Kuyinto efanelekayo lapho abantu wabona isakhiwo sokucwaninga. I-brain ye-organisational yathunyelwe phakathi kwezinhlayiyana zezinhlayiyana, eyakhelwe ngokuchofoza ngokusebenzisa ukuxhumana. Ngokuqhathanisa ukuthi izinhlelo zokusebenza kwe-AI, futhi sinikeze lokho. Sitholela amamodeli ukulawula ukubuyekeza ngaphandle kokufika ku-precedence. Kuyinto njenge-training ye-advocate ku-judgments ngaphandle kwe-course law. Umbhali we-config ushiye timeout = 30s. It ushiye timeout = 5s. Umbhali wahlanganyela. Yini? I-git blame ibonisa umuntu. Umbhali wahlanganyela. Ukulungiswa okuhlobene. I-CRM inikeza "ukudluliselwa kwangapheli." I-Enterprise software iyatholakala kakhulu ku-storage state, kodwa akuyona kakhulu ekhukhwini izixazululo. Uninzi lwezinhlelo ungacazulule ukuthi okufanayo ngoku futhi okufakiwe, kodwa akuyona ukuthi kungcono ukuthi inguqulo yakhelwe ngexesha elandelayo—izinto zokufaka zihlanganisa, ukuthi izinzuzo zihlanganisa, futhi ukuthi i-compromise yenza imiphumela. Ngakho-ke, "ukudibanisa i-LLM ku-systems yakho" isizukulwane: isampula ingathola idatha, kodwa akufanele ukubonisa isisombululo se-organization. Uma ufuna I-AI ukwenza ngokuvumelana, ufuna indlela yokwakhiwa okungagunyaziwe kuphela kwegama, kodwa ukuhlangabezana ukuthi ivumela i-state ku-action. Isilinganiso se-value-layer akuyona ama-documents; kuyinto izixazululo ezisetshenziselwa ama-documents kanye ne-how those documents were created. Ngokwenza kanjani umlingani wahlanganisa ukuthi-earn-out? Ngaba u-analytic wahlukanise le-risk? Yintoni okwenza u-clinic abahlukanisa kusuka ku-protocol? Izimpendulo izimpendulo zihlanganisa lapho izimboni zokusebenza ngokuvamile. Kodwa izimpendulo izimpendulo zihlanganisa engaphezulu kakhulu kunoma imibhalo. Ungathola ama-pseudonym entities. Ungathola ngempumelelo izimpendulo zihlanganisa ngempumelelo. "Ukuvame ngokuvamile umugqa obuningi lapho umlawuli we-counterparty iyatholakala ku-X-firm" iveza into ngisho nangokuthi i-X i-masked. Yintoni i-Context Graphs Enkosi I-trillion-dollar platforms elilandelayo ngeke ikhiqizwa ngokongezwa kwe-AI kuma-system eyenziwe ngegama, kodwa ngokufaka i-reasoning eyenza idatha ku-action e-context chart. I-context chart ikhiqiza into eyenza i-system of record ngokuvamile: i-history, i-"why", i-"how did we get here?" Uma i-context charts ibonise isakhiwo efanelekayo, kungenziwa isakhiwo sehlabathi. Zihlanganisa isakhiwo se-organizational—decision dynamics, uvavanyo se-state, ukuxhumana kwe-entity. Ungasebenza izinhlamvu noma izivivinyo ngaphakathi kwezimodeli zayo. Ungasiza "ke lapho?" futhi ufumane imibuzo enhle, futhi akuyona i-hallucinations emangalisayo, ngoba ungasungula into emangalisayo. I-context graph is a graph of nouns; it is a graph of decisions with evidence, ukucindezeleka, futhi imiphumela. "I-Context Graph" ivumelele lapho ungahambisa izinsizakalo ezimbonini ezivamile: akuyona kuphela iziganeko, kodwa izixazululo nge-evidence ezokufinyelelekayo, izinzuzo ezinzima, i-tradeoff ebonakalayo, futhi into ezilandelayo. Ngaphandle kwalokho, uzothola noma imodeli enhle enezimbonini, noma i-firehose ye-activity eyenziwa okungagunyaziwe. Yini lokhu kubona ngokuvamile? Umthengisi we-renewal inikeza ukunciphisa i-20%. I-Policy Caps inikeza i-10% ngaphandle kwe-service-impact exception. Umthengisi inikeza izimo ezintathu ze-SEV-1 kusuka ku-PagerDuty, isixazululo se-open "cancel unless fixed" ku-Zendesk, kanye ne-pre-renewal thread lapho i-VP yasungulwa isixazululo esifanayo ku-quarter edlule. I-Routes isixazululo ku-Finances. I-Finance isixazululo. I-CRM isixazululo se-one fact: "20% discount." Uma unayo idatha yama-decision, i-"why" ithatha idatha yama-first-class. Ngaphezu kwalokho, lezi idatha zihlanganisa ngokwemvelo i-context chart: ama-entities e-business akuyona (i-accounts, ama-renewals, ama-tickets, ama-incidents, ama-policy, ama-approveers, ama-agent runs) ezihlangene ngama-decision events (ama-moments eyenza) kanye ne-"why" ama-links. Amabhizinisi angakwazi ngokuzenzakalelayo i-audit kanye ne-debug autonomy futhi ukuguqulwa ama-exceptions ku-precedent ngaphandle kokufunda kwelinye imizuzu e-Slack. I-feedback loop inikeza lokhu ingxubevange. Izimpendulo ezivela ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye zibe. Ngaphandle kwalokho, akuvame ukufinyelela okungagunyaziwe. Lokhu kuqala nge-human-in-the-loop: umphakeli inikeza, ukuthatha umklamo, ukuguqulwa izivakashi, futhi ibhalisele izimpendulo. Ngesikhathi eside, njengoba izimo ezivamile zihlanganisa, iningi kunazo ku-automated ngoba uhlelo inesibuyekezo esakhiwo sokufundisa izixazululo ezidlulileyo kanye nezinhlangano. Ngaphandle kwalokho uma umntu akufunda, i-diagram ivame ukwandisa, ngoba isakhiwo se-workflow ithatha ingxubevakeli, isivakashi, kanye nokuqinisekisa njengama-precedence esiyingeni. Uma i-context charts iyiphi okungenani ebalulekile, ngoko ke akuyona ngaphezulu? I-Why Context Graphs Are Rare: I-Five Coordinate System Problem I-Context Graphs ayikho ngokwenene emangalisayo namhlanje ngenxa yokufuna ama-joins phakathi kwama-koordinate izinhlelo ezahlukile ezahlukile. I-databases ezivamile ezahlukile zihlanganisa izinsuku ezedlule. Unayo i-customer_id, i-order_id, ingozi yokuhlanganisa. I-join yi-discreet; ama-keys zihlanganisa; isebenze kahle. I-reasoning ye-organizational inikeza uhlobo olahlukile yokuhlanganisa. Ungafuna ukuxhumanisa: into efanayo (iziganeko) ku-akhawunti (timeline) ku-imeyili (semantics) ku-imeyili (i-attribution) ku-imeyili (i-resultate). Lezi Izinhlelo ezingu-5, izinhlobo ezingu-5 zihlanganisa: I-Timeline ifumaneka: Ukuxhumanisa isikhathi eside. I-config is 30s now. It was 5s last Tuesday. Ukuxhumanisa lezi zihlanganisa ukuxhumanisa isikhathi lapho "ngemuva" futhi "ngemuva" zihlanganisa izinga lokuqala, akuyona amafutha. I-Event Joins: I-Connecting Events ku-Sequences. Ukusebenza kwangaphakathi, bese i-Alarm eyenziwe, bese i-Rollback. I-Ording matters. I-Cause-relevant windows matters.I-United condition iyona i-proximity ku-event-space, engaphandle kwe-key equality. I-Semantic Joins: Ukuxhumanisa imibuzo phakathi kwezakhiwo. "I-Churn risk" ku-support ticket ibhekwa ku-"retension concern" ku-sales note. I-join iyinhlanganisela ye-vector, futhi akuyona i-string matching. I-fuzzy ngokwemvelo. I-Attribution Joins: Ukuxhumanisa izindlela ze-actors ku-proprietary. Who has approved this? Who owns that decision? I-Attribution crosses org structure, izinga lokuzonwabisa, izinga lokuzonwabisa. I-topology ngokuvamile kuyinto izimo yokuxhumana. Iziphumo zihlanganisa: Ukuxhumanisa izixazululo iziphumo. Lezi zihlanganisa zihlanganisa zihlanganisa ukuthi imiphumela yokuthengisa. I-joining iyinhlangano, akuyinkimbinkimbi. Kufuneka ukuchithwa kwe-contra-factual: Yintoni kungenzeka nangaphandle? Yonke uhlobo le-joining ine-geometry eyahlukile. I-timeline iyahlukile. Izinhlangano zihlanganisa. I-semantics ivela emkhakheni we-vector. Ukuhlukaniswa yi-graph-structured. Iziphumo zihlanganisa yi-causal DAGs. I-contextual graph problem ivumelanisa lapho uye ufunde ukuthi ungayenza kwabo ngempumelelo; zihlanganisa kanjani ama-agents ne-umuntu ukuxhumana. Indlela I-Context Graphs Yenza I-Tractable: I-Agent Trajectories njengezinto ze-Training Umbala we-context graphs iyatholakala ngoku kuba singakwazi ukufundisa uhlelo lwe-coordinate efakiwe lapho lezi zihlanganisa ziye ziye zibonakalayo. I-Agent Trajectories (ngokususa ukuyisebenzisa umsebenzi olusebenzayo) iyisisignali yokufunda. Uma i-Agent isixazulule ingozi, ivumela zonke izigaba ezingu-5 zokuxhumana ngokuzimela. It isixazululo entities phakathi kwezakhiwo. It isixazululo imiphumela. It isixazululo ingozi. It isixazululo isixazululo. It isixazululo imiphumela. I-Trajectory kuyinto isampula sokuphumelela kwe-multi-coordinate joins. Ukubuyekeza izindlela ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenalutho ezingenal Konke ukuhlanganiswa okuhlobene ne-joining ye-arbitrary predicates ku-coordinate systems. I-context charts ayenziwe ngoba ukuxhuma ku-five different geometries nge-fluid keys kubalulekile ukufundisa umphumela wahlukaniswa kusuka ku-operational data. I-agent trajectories inikeza ukuthi idatha, imathematics manje kukhona, futhi i-agent ergonomics iyatholakala kuphela emkhakheni. Ukuhamba kwelizwe (ukushintshwa ngempumelelo) ukufundisa i-homophily - ama-nodes zihlanganisa ngenxa yokuxhumana. I-global walks (ukushintshwa ngaphandle) ukufundisa ukuxhumana kwezakhiwo - ama-nodes zihlanganisa ngenxa yokudlala ama-rolls ezivamile, ngaphandle kokufaka ngqo. Qinisekisa izimboni ezimbili eziphambili kumakhasimende. Omunye isebenza ngezimali, omnye ku-notifications. Akukho amakethe ezahlukile, akukho ikhodi elihlukile, akukho amakethe e-Slack ezivamile. I-Homophily akukwazi ukubonisa kwabo njengama-similar. Kodwa ngokwemakhiwo zihlukile – umdlalo efanayo kwi-subgraphs ezahlukile, imibuzo efanayo, izindlela ezivamile zokuphumelela. I-Equivalence ye-Structural ibonisa lokhu. I-Agents iyahambisana ne-informed (not random) walkers. Uma i-agent idinga ingozi noma ivule umsebenzi, ivula indawo yesimo se-organizational. I-agent idinga izinhlelo, ukhangela idatha, ivumela i-API. I-trajectory yindlela yokuhamba nge-diagram ye-entity ye-organizational. Ngokungafani ne-random walks, ama-agent trajectories zihlanganisa imiphumela. I-agent iyahlanganyela ngokuvumelana ne-imeyile. Ukuhlola isivumelwano sokukhiqiza, kungenzeka ukuqala ngokubanzi-ukushintshwa okwesikhathi esidlulile kuzo zonke izinhlelo? Kuyinto ukuhlola jikelele, indawo yokuhlanganisa. Njengoba izitifiketi zihlanganisa, kulinganiswa izinsizakalo ezithile, izivumelwano ezithile sokusungula, izindlela ezithile zokuhambisana. Kuyinto ukuhlolwa yendawo, indawo yama-homophily. Ukusuka okuhlobisa isakhiwo ngokusebenzisa ukubuyekezwa kwe-brutto-force. Ukusuka okuhlobisa isakhiwo ngokusebenzisa ukubuyekezwa kwe-problem-directed. Umthengisi uye lapho isakhiwo ihamba, futhi imiphumela ibonisa ukuthi kungcono kakhulu. Ngokusebenza ngokufanelekileyo, izindlela ze-agent ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye zibe. Ngamunye i-trajectory isampula isakhiwo se-organization, esihlalweni ku-parts esiyingqayizivele yokusebenza esiyingqayizivele. I-accumulate thousands and you get a learned representation of how the organization functions, discovered through use. I-ontology ifakwe kusuka ku-walks. I-entities ebonakalayo kubangelwa kubangelwa kubangelwa kubangelwa kubangelwa kubangelwa kubangelwa kubangelwa kubangelwa kubangelwa kubangelwa kubangelwa kubangelwa kubangelwa kubangelwa kubangelwa kubangelwa kubangelwa kubangelwa kubangelwa. I-Elegance ye-economic etholakalayo. Amadivayisi akuyona i-context chart – ama-solving ama-problems eyenza ukuguqulwa. I-context chart iyona-exhaust. I-context engcono ivumela ama-agents ezinzima; ama-agents ezinzima zithunyelwe ngaphezulu; ukuthuthukiswa ikhiqiza i-trajectories; ama-trajectories zithunyelwe i-context. Kodwa lokhu kusebenza kuphela uma ama-agents akufanele umsebenzi esizayo. Ngesikhathi eside, njengoba i-context charts ibonise ulwazi olufanelekayo, kungenziwa into enhle: isampula esizayo esizayo esizayo. I-Context Graphs Ingaba Yenza I-Model ye-Production World I-model yokukhiqizwa ye-world is a learned, compressed representation of how an environment works. It encodes dynamics, i.e. what happens when you take actions suspended in a specific state. It captures structure: what entities exist and how they relate. Futhi it enikezela ukubuyekeza: ngokuvumelana nesimo se-akhawunti, kanye nesimo se-akhawunti ebonakalayo, ukuthi kuyinto elilandelayo? Izimodeli zehlabathi zibonisa into ebalulekile: ama-agents angakwazi ukufundisa ama-representations ezicindezelwe ze-environments futhi ukufundisa ngokuphelele ngaphakathi kwe-"dreams" - izitimela ezilinganiselwe nge-space emangalisayo. I-world model ivela ku-simulator. Ungasebenza ama-hypotheticals kanye nokufumana imibuzo enhle ngaphandle kokusebenza emkhakheni. Kuyinto analogi okuhlobene robotics. A world model capturing physics (how objects fall, how forces propagate) ungahambisa imisebenzi robot ngaphambi kokwenza kwabo, ukuqeqesha amaphilisi emangalisayo, ukuhlola amaphilisi emangalisayo ngokushesha, futhi ukuguqulwa hardware physics. I-better your physics model, the more useful your simulations. I-logic efanayo isetshenziselwa izinhlelo, kodwa i-physics iyahlukile. I-physics ye-Organization ayikho i-mass and momentum. Kuyinto i-dynamics ye-decision. Ukulungiselela kanjani ama-exceptions? Indlela yokukhula ama-escalations? Yini kuza lapho ukuguqulwa le-configuration ngenkathi i-feature flag iyahambisana? Yini i-blast radius ye-deploying ku-this service ngokuvumelana ne-dependence yamanje? I-Event Clock ibonisa indlela yokusebenza kwe-system - futhi isebenzayo kuyinto ukuthi kufanele ukuguqulwa. I-context graph with enough accumulated structure becomes a world model for organizational physics. It encodes how decisions unfold, how state changes propagate, how entities interact. Uma unayo lokhu, ungakwazi ukucubungula. Ngama-PlayerZero, sinikeza imizamo ye-code—ukudlulisela izinguquko ezihambisanayo kumamodeli yethu yama-systems zokukhiqiza kanye nokuqondisa imiphumela. Ngokusho nokuguqulwa okwenziwe, izakhiwo ezivamile kanye nezinhlangano zokusebenza, imizamo yokusebenza kwamakhasimende: kungenzeka ukuthi okuhlobisa yini? Yini indlela yokukhangisa? Wonke amakhasimende zihlanganisa? Lezi zibonelelo zikhwama. Lezi zibonelelo phezu isakhiwo esihlalweni. Sihlola izindlela eziliqinsi ngokusebenzisa imiphumela yokukhiqiza ukufundisa izakhiwo – ukuthi izindlela ze-code zikhwama, ukuthi izakhiwo zihlanganisa ngokumangalisayo, ukuthi izigaba zokusebenza zihlanganisa imiphumela. I-world model ikhodi lokhu. I-simulation ikhohlisa imodeli nge-hypotheticals. I-simulation iyisisombululo sokufuna. Uma i-context chart yakho ayikwazi ukujabulela "isib.", kuyinto kuphela i-index yokufunda. Implications for the Continual Learning Debate Imiphumela ye-Continuous Learning Debate Iningi abantu akutholwe I-AI ayihlabathi isebenzo ngoba amamodeli akwazi ukufundisa kwebhizinisi - sinikeza ukwakhiwa kwe-custom training loops for every capability, okuyinto akufinyelela ekupheleni elide yobuchwepheshe yobuchwepheshe. I-diagnosis iyatholakala. Kodwa yini uma i-framing ye-standard kuyinto ukuchithwa? Ukufundwa okuqhubekayo ibheka: kanjani ukuguqulwa ama-weights kusuka ku-experience eqhubekayo? Kuyinto enhle-ukudluliselwa okucacisa, ukuchithwa kwe-distribution, ukuqeqeshwa okuphakeme. I-Models yeHlabathi ibonise i-alternative: ukugcina isampula, ukuphucula isampula seHlabathi. I-Model ayidinga ukufundisa uma isampula seHlabathi ivela. Kuyinto ukuthi ama-agents angakwazi ukwenza nge-graphs ye-context eyenziwe. Yonke i-trajectory kuyinto umzekelo we-dynamics ye-organizational. Ngesikhathi sokuphendula, ukwenza ukuhlaziywa phezu kwesi-evidence: Ngokusho yonke into esithathwe mayelana ne-how this system behaves, Ngokusho ama-observations ezivamile, lokho kuyinto emzimbeni esizayo kulabo? Izinto zokusebenza? Ngaphezu kwama-trajectories, ukuhlaziywa okungcono. Akukho ngenxa yobugcisa imodeli, kodwa ngenxa yobugcisa imodeli emhlabeni. Futhi ngoba umzila wehlabathi inikeza ukucubungula, uzothola into enhle kakhulu: ukucubungula okucubungula. Akukho kuphela "ukungeke eminye izimo ezivamile?" kodwa "ukungeke ukuthi ngifake lokhu?" I-agent inikeza imizuzu, i-evaluates, ukhethe ngokuvumelana. Kuyinto abasebenzi abanolwazi kukhona ukuthi abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli abalandeli. I-path to economically transformative AI kungabangela ukulungiselela ukulungiselela okuqhubekayo. It kungabangela ukwakha amamodeli yomhlaba okuvumela amamodeli amamodeli asebenza njengokufunda, ngokusebenzisa ukwandisa ama-evidence bases kanye ne-inference-time computing ukuze zibonele futhi zibonele phezu kwabo. Model is the engine. I-context graph is the world model that makes the engine useful. I-context graph is the model that makes the engine useful. Isisindo esisodwa esisekelwe kumamodeli yomhlaba kukhona i-ontologies ephelele, ngakho-ke kuncike ukuhlola i-ontologies esifundeni kanye ne-learned. I-Prescribed vs. I-Ontologies Ebhaliswe: Izindlela ezimbini ze-Organization Structure Iningi abantu akufinyelela inkinga ukuthi isithombe se-context isithombe isithombe se-graph database noma isithombe se-structured. Lokhu akuyona. I-context graphs inikeza indlela esisodwa esisodwa sokubambisana ne-schema ne-representation. Kuyinto ebalulekile njengoba amabhizinisi zihlanganisa izixhobo ezaziwayo (Neo4j, i-vektor stores, i-knowledge graphs) futhi zihlanganisa ukuthi ama-agents zabo akuyona ezingenalutho. Ama-primitives akuyona. I-"Ontology" iyisici enhle. Kukhona i-ontologies ezihlaziywa (i-regulatory enginesha, i-workflows, i-governance layers). I-Palantir yasungulwa inkampani ye-$50B kulesi: isigaba esifanele sokubhalisa idatha se-enterprise ku-objects ne-relationships. Uyakwazi ukucacisa isikhwama. Uyakwazi ukuxhaswa. It isebenza uma ufunde isakhiwo ngaphambi. Inkampani elilandelayo ye-$ 50B iyakhelwe kwi-ontologies ezaziwa. Isakhiwo esithatholakala kanjani umsebenzi ngokwenene, futhi akuyona kungcono ukuthi kungenziwa. Lokhu kubalulekile ngenxa yokuba kunezinto ezininzi ezihambayo ekusungululweni ukuthi asikwazi ngayinye isikhathi, futhi ama-agents zihlanganisa ukubuyekezwa kwethu! I-AI ye-Enterprise kufuneka uqhagamshelane nabanye. Kukho izinzuzo ezininzi ze-ontologies ezihlaziywa. Akukho cishe isakhiwo yokufunda, ukwakhiwa, nokuvakashela izinzuzo ezihlaziywayo. Izinzuzo ezihlaziywayo (izakhiwo ezihlaziywayo ezihlaziywayo, izinzuzo ezihlaziywayo ezihlaziywayo ezihlaziywayo ezihlaziywayo ezihlaziywayo) kuyinto ingcindezi. Futhi lokhu kungenzeka ukuthi ingcindezi akuyona inkinga. I-Memory inikeza ukuthi uyazi ukuthi kufanele ibhekwa futhi indlela yokufaka. Kodwa inkulumo enhle kakhulu kuyinto isakhiwo esaziwa ngaphambi kokufika ku-agents nge-use. Enye ingcindezi okungenani: "I-decision tracks iyiphi i-trajectory logs kuphela." Kuyinto njengokuthi wathi ama-embeddings iyiphi i-keyword indexes kuphela. I-technically adjacent, i-conceptually wrong. Ngingathanda lapho ama-embeddings babona ubuchwepheshe e-aliens? A indlela e-probability to represent similarity that made the "solved" problem of fuzzy search look prehistoric. Abantu wabhala, "Kungabikho lokhu lapho i-Elasticsearch?" Thina ngexesha elifanayo lokuphendula yokufundisa isakhiwo. I-Trajectory logs ibhalisele ukuthi kwenzeka. I-Decision traces (ukwenza ngokufanele) ibhalisele ukuthi kungenzeka. Izinto ezithakazelisayo. Izakhiwo ezithakazelisayo. Izakhiwo ezithakazelisayo. Indlela yokucubungula inikeza nge-organizational state space. Umbala: i-logs i-append-only records. I-decision tracks i-tracking data ye-production world models. I-schema ayikho into esithathwe ngokushesha. I-schema ifakwe kusuka ku-walks. Konke lokhu kungabangela kakhulu academic noma hypothetical, kodwa graphs konteksthi kukhona emangalisayo namhlanje, futhi kuyoba ngokushesha kwangaphambili. Uma i-Context Graphs ikakhulukazi ifakwe I-context chart ivumelele lapho ungayifaka izinsiza ezivamile ezivamile: akuyona kuphela iziganeko, kodwa izixazululo nge-evidence eyenziwe, izinzuzo ezivumelanayo, i-tradeoff ebonakalayo, kanye neminye izinto ezilandelayo. Ngaphandle kwalokho, uzothola imodeli enhle engazimbela umsebenzi, noma i-firehose ye-activity eyenziwa okungagunyaziwe. Okokuqala, umphumela wokufaka kufanele kuhlobene. Ezinye izindinganiso zihlanganisa "ukudluliselwa": izivakashi ze-triage, izivakashi ze-reassignments, izivakashi ze-deviation, izivakashi ze-escalation ezivela ku-"we're doing X." Lezi zihlanganisa ngenxa yokuba kukhona isixazululo se-deliberation ne-engxazululo. Ezinye izimo zihlanganisa phakathi kwezivakashi ze-half-decisions kanye nezivakashi ze-reversible. Uma ungenza ukucacisa ukuthi ngokuvamile zihlanganisa njengesixazululo, ungaphindezisa ukucaciswa kwe-noise kunomthombo. Lokhu lapho izinzuzo eziningi ze-generic "process min Okokuqala, ukucubungula ukucubungula kubalulekile ngenxa yokubonisa ukuthi kungcono ukuthi kunzima ukufumana izimpendulo izimpendulo. Lezi zimpendulo zihlanganisa ngokuvamile ngamazwe. Kwezinye izimo, izimpendulo zihlanganisa ku-software, ngakho izimpendulo zihlanganisa ngokwemvelo. Kwezinye, ukucubungula ngokuvamile zihlanganisa ngokuvamile: ku-escalations, i-handoffs, i-dispatch calls, i-re-planning i-huddles, i-negotiations. Ngakho-ke, i-voice kuyinto ukucubungula kwezimboni eziningi ze-physical-world: kuvumela ukucubungula izingxenye ze-decision ye-verbal ngokuvamile, ngaphandle kokuqinisekisa abantu Okwesibini, ukuchitha kuphela akuyona akuyona. I-context captured ingangena, i-stale, noma ngempumelelo. I-context charts ivela izimpuziswano zokusebenza: ukuhlaziywa okuphumelela okuphumelela, izixazululo ezibhekiselele ngokubhekiselele okuphumelela ngosuku, ama-assumptions eyenza ukuba zibe zibonakalayo kodwa asikaze zibonakaliwe. Ukuzinza kwe-ontology kubaluleke futhi, kodwa imiphumela yayo ingahlukile, futhi lokhu kuyinto lapho ibhizinisi ibhizinisi. Ngo-asset-heavy domains, isakhiwo esifundeni yehlabathi kuyinto ephakeme ngokuvamile. Ngakho-ke i-ontology-first platforms isebenza ngokuvamile. Kodwa lezi zithombe ziye zihlanganisa ukulayisha isitimela ephakeme-up-front ngoba isakhiwo esiyingqayizivele se-decision ayatholakala ngokuqondile ngesikhathi esifanayo. Isikhathi kuyinto ukugcina isakhiwo, kodwa ukongeza isikhunta esifundeni: ukwelashwa imodeli esifundeni njenge-staffolding, futhi sicela izimpendulo zihlanganisa inkqubo ngokuvamile ukuthi izixazululo zenza ngokuvamile. Ngesikhathi eside, izivakashi ziye zihlanganisa ngezinyuko zokusebenza ngokufanelekileyo futhi zihlanganisa kakhulu nge-precedent Ngo-tech, ingcindezi olulodwa kubonisa. I-ontologies ayinambuzane ngenxa yokuba ibhizinisi ngokuvamile ibhizinisi. Imikhiqizo ibhizinisi futhi izici zihlanganisa. I-team ibhizinisi. I-go-to-market izindlela zihlanganisa. Amamodeli amayunithi amasha asebenza, amamodeli amasha asuswe. Ngaphandle kwe-B2B, imikhiqizo ye-B2B emakethe, emakethe ye-B2B emakethe, emakethe ye-B2B emakethe, emakethe ye-B2B emakethe, emakethe ye-B2B emakethe, emakethe ye-B2B emakethe, emakethe ye-B2B emakethe, emakethe ye-B2B emakethe, emakethe ye-B2B emakethe, emakethe ye-B2B emakethe. Ukuhlukaniswa okuhlobene ku-disalignment. Izinhlelo ezihlukahlukene zebhizinisi zihlanganisa izinguquko ezahlukile zayo: izibuyekezo zokusebenza ze-strateji, izibuyekezo ze-metric ezahlukile, izinhlelo ezahlukile ezahlukile ngokuzimela, izincwajana ezahlukile ezahlukile ezahlukile nezidingo ze-product. E-human-only organizations, okuhlobene nezivumelwano kanye nokushisa. E-agent organizations, okuhlobisa ngokushesha, ngoba ama-agents zihlanganisa ngezinye isigaba. I-contradictory context akhiqiza imiphumela engcono kakhulu, okuholela umsebenzi oluthile, ukulungiswa okusheshayo, kanye nezinqubo ezahlukile ezahluk Ngesikhathi eside, into enhle ye-organisation akhiqiza akuyona idatha. Kuyinto ukwakhiwa kwezimfuneko. I-patterns eyakhiweyo yindlela yokusungula okwenziwe ngokuvamile: ukuthi izakhiwo zihlanganisa, ukuthi izincazelo zihlanganisa, ukuthi izincazelo zihlanganisa, ukuthi izincazelo zihlanganisa zihlanganisa zihlanganisa IP ye-organisations. Kuyinto ebhizinisi yokusebenza kwebhizinisi, futhi namhlanje ibhizinisi ngokuvamile abantu futhi ekupheleni kwehlabathi. I-application companies has a opening because they sit on the decision surface. If you can capture judgment as a byproduct of execution and keep it current, ungakwazi ukwakha i-context graph: ukubuyekeza ukubuyekeza ukubuyekeza ukuthi ivimbele. I-Instrument Decisions, bese uqhagamshelane i-layer eyenziwe. Kukho i-inversion eyenza kakhulu ngokushesha. Ngaphandle kokubizwa kwehlabathi okokuqala, uzothola imiphumela eminyakeni lapho i-commit, futhi ufunde indlela yokucubungula isetshenziswe emzimbeni. Uma ukuthatha isixazululo, uzothola imikhiqizo etholakalayo, izimpendulo ezisetshenzisiwe, isivumelwano esekelwe, isivumelwano esekelwe, futhi indlela esekelwe ngokushesha. Ngemuva kwalokho, lezi zihlanganisa ku-memory of how decisions are actually made. Kuyinto akubuyekeza i-ontology ye-formal, futhi akuyona ngokushesha. I-model ebonakalayo iyatholakala kumadivayisi we-semantics, i-state, ne-hard constraints. I-part eyenziwa yi-layer ontology-i-first platforms akukwazi ukufinyelela okungagunyaziwe: ama-constraints amancane, ama-exception patterns, kanye ne-heuristics amancane ebonakalayo ezivela imiphumela. Ngo-healthcare, inkqubo uyazi ukuthi isivumelwano sokushicilelwa ngaphambi kokufakwa. It ayazi isampula ekubunjini ukuthi umdlavuza umdlavuza eminyakeni ezintathu noma amehora ezintathu: ukuthi ifomati yedokumentation umdlali isabela, lapho izivumelwano flip, lapho peer-to-peer kufanele ifakwe proactively, futhi ukuthi "izinyathelo ze-standard" zihlala. Le logic ayikho ku-scheme. Kuyinto emzimbeni yokufakelwa kwe-organisation. Ngaphandle kokuthunyelwe kwebhizinisi, ungakwazi ukuqala nge-substrate encane futhi uvumela isakhiwo se-value enezingeni eliphezulu ukufinyelela ngezinsizakalo zayo. I-value compounds ngoba zonke izicelo ze-edge ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye zibe. Uninzi lwesistimu unokufundisa ukuthi okufanayo; engaphansi kunazo ukuguqulwa ukuthi kungcono ukuthi okufanayo ngexesha lokufanele. I-context graph is a graph of nouns; it is a graph of decisions with evidence, constraints, and outcomes. Ukulungiselela kuqala ukuthi kusekelwe kumazwe owenziwe idatha, izinhlelo zebhisi, kodwa kunokwenzeka ukuthi kusekelwe kwebhisi olandelayo. Why Incumbents Ayikwazi Ukwakha I-Context Graphs Ngamanye ama-optimistic ukuthi abacwaningi ezivela ku-architecture. Warehouses ziye ziye ziye "i-truth registries," kanti CRM ziye ziye ziye "imishini ye-state nge-API." Lokhu kuyinto umbhalo we-evolution, akuyona ukuguqulwa. Kuyinto ingasebenza ukwenza idatha esiyingqayizivele engatholakali kakhulu. It akufanele ukuthatha izimpendulo. Izinto ezisebenzayo zihlanganisa futhi zihlanganisa indawo yamanje. I-Salesforce inikeza i-Agentforce, i-ServiceNow inikeza i-Now Assist, ne-Workday inikeza ama-agents ye-HR. I-Pitch yayo i-“we have the data, now we add the intelligence.” Kodwa lezi ama-agents zihlala izinzuzo zokusungula zayo. I-Salesforce isekelwe ku-state-state storage: ibonakalisa indlela yokufinyelela ngoku, futhi akuyona indlela yokufinyelela lapho ukuthatha isixazululo. Uma i-discount iyakutholwe, i-context esithakazelisayo akuyona akuyona. Unemibuzo yokufinyelela indawo yehlabathi ngesikhathi sokufinyelela, okungenza ukuthi unemibuzo akufinyelela, ukufundisa kusuka kwalo, noma usebenzisa njenge-precedence. Zihlala futhi iziphakamiso ze-parent blind spots. Ukusebenza kwe-support akuyona kuphela ku-Zendesk. Kulingana ne-customer-tier kusuka ku-CRM, izimo ze-SLA kusuka ku-facturing, izixazululo ezidlulile ze-PagerDuty, kanye ne-Slack thread flagging churn risk. Akukho umlinganiselo wabhala lokhu ngoba akukho umlinganiselo wabhala emzimbeni emibi. I-DevOps ihamba ngenxa yokuthengisa, izimali, ukuhweba, nokuphumelela kwamakhasimende. I-DevOps ihamba ngenxa yokuthuthukiswa, IT, nokuthuthukiswa. I-Security Ops ihamba phakathi kwe-IT, i-engineering, ne-compliance. Lezi "I-glue" izici zihlanganisa. Zihlanganisa ngokuvamile ngenxa yokuba akukho uhlelo elilodwa le-record akhiqiza inqubo ye-cross-functional workflow. I-org chart ikhiqiza inkulumo yokuthumela inkulumo okuyinto software akhawunti. I-agent enikezela inkulumo yayo akuyona kuphela iminyango embalwa. Inokuvumela ukuchitha izixazululo, izixazululo, kanye nezinhlangano ezisekelwe ukukhiqiza. Kuyinto indlela yokufinyelela kwinkqubo entsha ye-record: hhayi ngokuvimbela i-incumbent, kodwa ngokuvimbela i-category ye-truth eyenziwa kuphela uma ama-agents zihlala ku-workflow. Ukuphakamisa konke okuhle, lokho kuncike ngokuvamile kumakhasimende namhlanje? Umbuzo akuyona ukuthi izinhlelo ze-record zihlala - ngeke. Umbuzo kungenziwa ukuthi ama-platform ezilandelayo ze-trillion-dollar zisekelwe ngokongezelela kwe-AI kumadokhumenti eyenziwe, noma ngokuvimbela izimpendulo ze-traces ezikhuthaza idatha. Yini kungcono: I-Three Hard Problems I-Context Graphs inikeza ukuguqulwa kwama-problems ezintathu: I-two clocks problem. Sihlanganisa isakhiwo se-trillion-dollar yama-state kanye nokuningi kokuzimela. I-event clock kufuneka isakhiwo. I-Schema njengoba i-output. Ungenza ukuhlaziywa kwe-ontology ye-organisation. I-Agent trajectories ibonise isakhiwo nge-problem-directed traversal. I-embeddings iyisisakhiwo, futhi akuyona i-semantic—ukufaka izindawo zokuxhumana kanye nezimo zokuxhumana, kodwa akuyona. Izimodeli zehlabathi, noma izinhlelo zokufaka. I-context graphs eyenza isakhiwo efanelekayo ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye zibe. Izinkampani ezifanayo ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye ziye zibe. Kuyinto ukulungiselela. Akukho amamodeli ezingcono. Isakhiwo esihle yokwenza intelligence ezisekelwe ukwanda.
