Dalam beberapa tahun terakhir, dunia teknologi mengalami perkembangan serta tingkat penggunaan yang luar biasa akibat perkembangan Generative AI. Kehadiran sistem seperti ChatGPT, Gemini, Grok, Claude, Deepseek, Qwen, Mistral, dll tidak hanya mengubah cara manusia berinteraksi dengan komputer, tetapi juga menggeser batas antara alat dan “teman berpikir / diskusi”. Bahkan kini, perkembangan tidak berhenti pada generative AI saja, tetapi mulai bergerak menuju AI Agent, dimana AI mampu bertindak secara mandiri, mengambil keputusan, dan menjalankan tugas kompleks.<img src="/uploads/content/1775802327724-24162545.webp">Gambar 1. Hype Cycle for Generative AI, 2025 (Sumber : Gartner.com)Fenomena ini memunculkan satu pertanyaan besar, apakah kita sedang berada dalam fase “AI Bubble” atau bagian dari evolusi teknologi yang memang tak terhindarkan?. Hasil pemikiran ini mencoba melihat fenomena tersebut secara reflektif serta tidak hanya dari sisi teknologi, tetapi juga dari perspektif sosial dan ekonomi.Konsep “AI Bubble” tidak dapat dilepaskan dari kerangka Gartner Hype Cycle, yang menjelaskan bahwa perkembangan teknologi umumnya melalui lima tahap utama, yaitu technology trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, dan plateau of productivity [1]. Model ini menggambarkan bagaimana ekspektasi terhadap teknologi baru cenderung meningkat secara signifikan sebelum mengalami penurunan, dan kemudian secara bertahap mencapai tingkat kematangan serta produktivitas yang stabil.<img src="/uploads/content/1775802366159-312163114.webp"> Gambar 2. Gartner Hype Cycle (Sumber : Forbes.com)Menariknya, beberapa penelitian menunjukkan bahwa generative AI berada di fase “peak of inflated expectations” yaitu kondisi di mana ekspektasi publik sangat tinggi, sering kali melampaui realitas teknologi [2]. Bahkan studi terbaru tentang tingkat pemaakian generative AI menunjukkan bahwa teknologi ini tidak hanya mengikuti siklus teknis, tetapi juga siklus emosional yang dimulai dari rasa antusiasme, kebingungan, hingga adaptasi [3]. Dalam konteks ini, AI bubble bukan sekadar fenomena ekonomi, tetapi juga fenomena sosial.Generative AI berkembang dengan kecepatan yang jarang terjadi dalam sejarah teknologi. Menurut laporan Stanford University, investasi global pada generative AI mencapai sekitar $33.9 miliar dan terus meningkat, sementara adopsi AI dalam organisasi melonjak dari 55% menjadi 78% hanya dalam satu tahun [4]. <img src="/uploads/content/1775802388991-400756143.webp">Gambar 3. Perusahaan Global dengan Investasi Terbesar dalam Pengembangan Artificial Intelligence (Sumber : Statista.com) Namun, kenaikan ini tidak hanya terjadi pada level teknologi, tetapi juga pada ekspektasi publik yang berkembang seperti : • AI akan menggantikan sebagian besar pekerjaan manusia • AI mampu berpikir seperti manusia • AI akan menjadi solusi universal untuk berbagai masalahPadahal, dalam praktiknya, AI masih memiliki keterbatasan mendasar seperti bias data, kesalahan (hallucination) dan ketergantungan pada input yang diberikan. Fenomena ini menunjukkan bahwa kondisi saat ini tidak hanya sedang menghadapi inovasi teknologi, tetapi juga gelombang persepsi kolektif yang sangat kuat. Realitas di Balik Hype: Antara Harapan dan Kekecewaan Meskipun hype sangat tinggi, pada kenyataannya implementasi AI sering kali tidak seindah ekspektasi. Beberapa laporan menunjukkan bahwa banyak proyek AI belum memberikan dampak signifikan dalam produktivitas atau profit. Hal ini memperkuat indikasi bahwa pasa masa ini mulai memasuki fase “disillusionment”, di mana organisasi mulai menyadari bahwa implementasi AI tidak semudah yang dibayangkan. Bahkan, banyak perusahaan menghadapi tantangan seperti: • Integrasi sistem yang kompleks • Kualitas data yang tidak memadai • Kebutuhan adaptasi organisasi • Risiko etika dan keamananFenomena ini sejalan dengan studi yang menyebutkan bahwa gelombang hype teknologi sering kali diikuti oleh fase koreksi sebelum mencapai kematangan [5]. Namun, penting untuk dicatat bahwa koreksi tidak berarti kegagalan melainkan proses menuju stabilitas.AI Agent: Evolusi Berikutnya atau Bubble Baru? Jika generative AI berfokus pada “menciptakan”, maka AI Agent melangkah lebih jauh yaitu “bertindak”. AI Agent adalah sistem yang tidak hanya menghasilkan output, tetapi juga dapat mengambil keputusan, menjalankan tugas berulang serta berinteraksi dengan sistem lain secara mandiri. Konsep ini membawa dampak besar terhadap masa depan pekerjaan dan organisasi. Namun, di sisi lain, AI Agent juga berpotensi menciptakan hype baru.Pertanyaannya: apakah AI Agent benar-benar siap secara teknis dan sosial? Sejarah menunjukkan bahwa setiap gelombang teknologi membawa janji besar namun tidak semuanya langsung terealisasi. Oleh karena itu, AI Agent bisa dilihat sebagai dua hal sekaligus yaitu Evolusi logis dari AI dan Potensi bubble berikutnya jika ekspektasi kembali tidak terkendali. Selanjutnya apakah ini bubble atau evolusi?. Jawaban dari pertanyaan ini tidak sesederhana “ya” atau “tidak”.Perkembangan AI saat ini memiliki fondasi yang lebih kuat dibandingkan era bubble sebelumnya seperti “dot-com”[6]. Investasi yang masuk tidak sepenuhnya dari sisi spekulatif, tetapi juga didorong oleh kebutuhan nyata dan potensi produktivitas [7]. Di sisi lain, pola hype yang terjadi sangat mirip dengan fenomena bubble dalam sejarah teknologi. Artinya, kemungkinan besar saat ini perkembangannya sedang berada dalam kondisi “hybrid”. Dimana ada elemen bubble (overhype, ekspektasi tinggi) dan elemen evolusi (teknologi nyata dan terus berkembang). Dengan kata lain, bukan teknologinya yang menjadi bubble tetapi cara manusia memaknainya.Euforia terhadap Generative AI dan AI Agent akhirnya menunjukkan bahwa yang sedang terjadi bukan sekadar kemajuan teknologi, melainkan refleksi dari harapan, ketakutan, dan ekspektasi manusia terhadap masa depan. Istilah “AI Bubble” mungkin terlalu sederhana, tetapi mengabaikan potensi overhype juga bukan tanpa risiko. Di tengah derasnya arus inovasi, yang dibutuhkan bukanlah sikap ekstrem yang berarti terlalu optimistis maupun terlalu skeptis, melainkan keseimbangan dalam melihat dan menyikapinya. AI perlu dipahami secara kritis, tanpa terjebak dalam hype, namun juga tanpa menutup diri terhadap potensinya. Pada akhirnya, arah perkembangan AI tidak hanya ditentukan oleh kecanggihan algoritma dan perangkat kerasnya, tetapi oleh pilihan manusia dalam menggunakannya yang dianggap sebagai alat, teman, atau sekadar ilusi.Referensi: [1] J. Werner, “The trough of disillusionment and four outliers on the Gartner hype cycle,” Forbes, Jul. 18, 2024. [Online]. Available: <a href="https://www.forbes.com/sites/johnwerner/2024/07/18/the-trough-of-disillusionment-and-four-outliers-on-the-gartner-hype-cycle/">https://www.forbes.com/sites/johnwerner/2024/07/18/the-trough-of-disillusionment-and-four-outliers-on-the-gartner-hype-cycle/</a> [2] R. Wandile, “Gartner places generative AI at the top of the hype cycle: What does it mean for the technology?,” LinkedIn, Sep. 12, 2023. [Online]. Available: <a href="https://www.linkedin.com/pulse/gartner-places-generative-ai-top-hype-cycle-what-does-rahul-wandile/">https://www.linkedin.com/pulse/gartner-places-generative-ai-top-hype-cycle-what-does-rahul-wandile/</a>.[3] V. Truong, “Hype and Adoption of Generative Artificial Intelligence Applications,” 2025, arXiv. <a href="https://doi.org/10.48550/arXiv.2504.18081">https://doi.org/10.48550/arXiv.2504.18081</a>.[4] Maslej, N., Fattorini, L., et al. (2025). Artificial Intelligence Index Report 2025 (Version 3). arXiv. <a href="https://doi.org/10.48550/ARXIV.2504.07139">https://doi.org/10.48550/ARXIV.2504.07139</a>.[5] Taherdoost, H. (2025). From hype to bubble: a historical analysis of technology trends and the case for artificial intelligence. Future Digital Technologies and Artificial Intelligence, 1(1), 1–6. <a href="https://doi.org/10.55670/fpll.fdtai.1.1.1">https://doi.org/10.55670/fpll.fdtai.1.1.1</a>.[6] Janus Henderson Investors, “AI versus the dotcom bubble: 8 reasons the AI wave is different,” Oct. 16, 2025. [Online]. Available: <a href="https://www.janushenderson.com/corporate/article/ai-versus-the-dotcom-bubble-8-reasons-the-ai-wave-is-different/">https://www.janushenderson.com/corporate/article/ai-versus-the-dotcom-bubble-8-reasons-the-ai-wave-is-different/</a>[7] E. Brynjolfsson, D. Rock, and C. Syverson, “Artificial Intelligence and the Modern Productivity Paradox,” The Economics of Artificial Intelligence. University of Chicago Press, pp. 23–60, 2019. <a href="https://doi.org/10.7208/chicago/9780226613475.003.0001">https://doi.org/10.7208/chicago/9780226613475.003.0001.</a>

In recent years, the tech industry has seen remarkable advancements and a surge in adoption driven by the rise of Generative AI. The emergence of systems such as ChatGPT, Gemini, Grok, Claude, Deepseek, Qwen, Mistral, and others has not only transformed how humans interact with computers but has also blurred the line between tools and “thinking/discussion partners.” Even now, advancements are not limited to generative AI alone but are shifting toward AI Agents, where AI can act autonomously, make decisions, and perform complex tasks.<img src="/uploads/content/1775802327724-24162545.webp">Figure 1. Hype Cycle for Generative AI, 2025 (Source: Gartner.com)This phenomenon raises a major question: are we currently in an “AI Bubble” phase, or part of an inevitable technological evolution? This analysis attempts to examine the phenomenon reflectively—not only from a technological perspective but also from social and economic viewpoints.The concept of the “AI Bubble” cannot be separated from the Gartner Hype Cycle framework, which explains that technological development generally goes through five main stages: technology trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, and plateau of productivity [1]. This model illustrates how expectations regarding new technologies tend to rise significantly before declining, and then gradually reach a stable level of maturity and productivity.<img src="/uploads/content/1775802366159-312163114.webp"> Figure 2. Gartner Hype Cycle (Source: Forbes.com)Interestingly, several studies indicate that generative AI is in the “peak of inflated expectations” phase—a stage where public expectations are extremely high, often exceeding the technology’s actual capabilities [2]. Even recent studies on generative AI adoption rates suggest that this technology follows not only a technical cycle but also an emotional cycle, ranging from enthusiasm to confusion and ultimately adaptation [3]. In this context, the AI bubble is not merely an economic phenomenon but also a social one.Generative AI is evolving at a pace rarely seen in the history of technology. According to a Stanford University report, global investment in generative AI has reached approximately $33.9 billion and continues to rise, while AI adoption within organizations has surged from 55% to 78% in just one year [4]. <img src="/uploads/content/1775802388991-400756143.webp">Figure 3. Global Companies with the Largest Investments in Artificial Intelligence Development (Source: Statista.com) However, this surge is not limited to the technological level but also extends to evolving public expectations, such as :• AI will replace most human jobs• AI is capable of thinking like humans• AI will become a universal solution to various problemsIn reality, however, AI still has fundamental limitations such as data bias, errors (hallucinations), and dependence on the provided input. This phenomenon indicates that the current landscape is not only facing technological innovation but also a powerful wave of collective perception. The Reality Behind the Hype: Between Hope and Disappointment Despite the intense hype, in reality, AI implementation often falls short of expectations. Several reports indicate that many AI projects have yet to deliver significant impacts on productivity or profit. This reinforces the indication that the current phase is entering a “disillusionment” stage, where organizations are beginning to realize that AI implementation is not as straightforward as imagined. In fact, many companies face challenges such as: • Complex system integration• Inadequate data quality• Organizational adaptation needs• Ethical and security risksThis phenomenon aligns with studies noting that waves of technological hype are often followed by a correction phase before reaching maturity [5]. However, it is important to note that correction does not mean failure but rather a process toward stability.AI Agents: The Next Evolution or a New Bubble? While generative AI focuses on “creating,” AI Agents go a step further by “acting.” An AI Agent is a system that not only generates output but can also make decisions, perform repetitive tasks, and interact with other systems autonomously. This concept has significant implications for the future of work and organizations. However, on the other hand, AI Agents also have the potential to create a new wave of hype.The question is: are AI Agents truly ready, both technically and socially? History shows that every wave of technology brings great promises, yet not all are immediately realized. Therefore, AI Agents can be viewed as two things at once: the logical evolution of AI and the potential for the next bubble if expectations once again spiral out of control. So, is this a bubble or an evolution? The answer to this question is not as simple as “yes” or “no.”Current AI development has a stronger foundation compared to previous bubble eras like the “dot-com” era[6]. The investments flowing in are not entirely speculative but are also driven by real needs and productivity potential[7]. On the other hand, the current hype patterns closely resemble historical technology bubble phenomena. This suggests that the current development is likely in a “hybrid” state—combining bubble elements (overhype, high expectations) with evolutionary elements (real, continuously advancing technology). In other words, it is not the technology itself that is the bubble, but how humans interpret it.The euphoria surrounding Generative AI and AI Agents ultimately reveals that what is unfolding is not merely technological progress, but a reflection of human hopes, fears, and expectations regarding the future. The term “AI Bubble” may be overly simplistic, but ignoring the potential for overhype is not without risk. Amid the torrent of innovation, what is needed is not an extreme stance—whether overly optimistic or overly skeptical—but a balanced approach to viewing and addressing it. AI must be understood critically, without getting caught up in the hype, yet without closing oneself off to its potential. Ultimately, the direction of AI’s development is determined not only by the sophistication of its algorithms and hardware, but by human choices in using it—whether as a tool, a companion, or merely an illusion.Reference: [1] J. Werner, “The trough of disillusionment and four outliers on the Gartner hype cycle,” Forbes, Jul. 18, 2024. [Online]. Available: <a href="https://www.forbes.com/sites/johnwerner/2024/07/18/the-trough-of-disillusionment-and-four-outliers-on-the-gartner-hype-cycle/">https://www.forbes.com/sites/johnwerner/2024/07/18/the-trough-of-disillusionment-and-four-outliers-on-the-gartner-hype-cycle/</a> [2] R. Wandile, “Gartner places generative AI at the top of the hype cycle: What does it mean for the technology?,” LinkedIn, Sep. 12, 2023. [Online]. Available: <a href="https://www.linkedin.com/pulse/gartner-places-generative-ai-top-hype-cycle-what-does-rahul-wandile/">https://www.linkedin.com/pulse/gartner-places-generative-ai-top-hype-cycle-what-does-rahul-wandile/</a>.[3] V. Truong, “Hype and Adoption of Generative Artificial Intelligence Applications,” 2025, arXiv. <a href="https://doi.org/10.48550/arXiv.2504.18081">https://doi.org/10.48550/arXiv.2504.18081</a>.[4] Maslej, N., Fattorini, L., et al. (2025). Artificial Intelligence Index Report 2025 (Version 3). arXiv. <a href="https://doi.org/10.48550/ARXIV.2504.07139">https://doi.org/10.48550/ARXIV.2504.07139</a>.[5] Taherdoost, H. (2025). From hype to bubble: a historical analysis of technology trends and the case for artificial intelligence. Future Digital Technologies and Artificial Intelligence, 1(1), 1–6. <a href="https://doi.org/10.55670/fpll.fdtai.1.1.1">https://doi.org/10.55670/fpll.fdtai.1.1.1</a>.[6] Janus Henderson Investors, “AI versus the dotcom bubble: 8 reasons the AI wave is different,” Oct. 16, 2025. [Online]. Available: <a href="https://www.janushenderson.com/corporate/article/ai-versus-the-dotcom-bubble-8-reasons-the-ai-wave-is-different/">https://www.janushenderson.com/corporate/article/ai-versus-the-dotcom-bubble-8-reasons-the-ai-wave-is-different/</a>[7] E. Brynjolfsson, D. Rock, and C. Syverson, “Artificial Intelligence and the Modern Productivity Paradox,” The Economics of Artificial Intelligence. University of Chicago Press, pp. 23–60, 2019. <a href="https://doi.org/10.7208/chicago/9780226613475.003.0001">https://doi.org/10.7208/chicago/9780226613475.003.0001.</a>

AI Bubble atau Evolusi ? Euforia Generative AI dan AI Agent

AI Bubble or Evolution? The Hype Surrounding Generative AI and AI Agents

Bayangkan sebuah masa depan di mana kecerdasan buatan bukan lagi sekadar teman mengobrol, melainkan asisten yang benar-benar bisa bekerja nyata untuk Anda. Kita sering bermimpi memiliki AI yang mampu mengelola kode di GitHub hingga menyusun jadwal di kalender secara otomatis. Namun, langkah besar ini membawa kekhawatiran baru: amankah data pribadi kita? Makalah ini memperkenalkan ZENITH, sebuah sistem orkestrasi AI generasi terbaru yang dirancang untuk menjawab keraguan tersebut. Fokus utama kami bukan hanya pada kecanggihan teknologi, tetapi pada cara menjaga kedaulatan identitas pengguna di setiap tindakan yang diambil oleh sistem. Melalui ZENITH, kami mengeksplorasi tantangan dalam membangun agen AI yang kuat namun tetap aman. Kami menyadari bahwa menyimpan kunci akses secara sembarangan sangat berbahaya di dunia digital saat ini. Oleh karena itu, ZENITH menggunakan pendekatan unik yang memisahkan tugas AI dari data sensitif pengguna. Dengan mengintegrasikan sistem keamanan tingkat tinggi seperti Auth0 Token Vault dan protokol verifikasi dua langkah yang real-time, kami memastikan bahwa AI tidak pernah menyentuh sandi asli Anda. Setiap tindakan penting tetap membutuhkan persetujuan manusia sebagai kendali utama. Hasilnya, ZENITH membuktikan bahwa kita bisa memiliki AI yang sangat membantu tanpa perlu mengorbankan privasi. Ini adalah cetak biru bagi teknologi masa depan yang cerdas, aman, dan tetap memanusiakan penggunanya.

Imagine a future where artificial intelligence is no longer just a chat companion, but an assistant that can actually do real work for you. We often dream of having AI capable of managing code on GitHub and automatically organizing schedules in our calendars. However, this major leap brings new concerns: is our personal data safe? This paper introduces ZENITH, a next-generation AI orchestration system designed to address these concerns. Our primary focus is not merely on technological sophistication, but on how to safeguard user identity sovereignty in every action taken by the system. Through ZENITH, we explore the challenges of building a powerful yet secure AI agent. We recognize that carelessly storing access keys is extremely dangerous in today’s digital world. Therefore, ZENITH employs a unique approach that separates AI tasks from users’ sensitive data. By integrating high-level security systems such as Auth0 Token Vault and real-time two-factor verification protocols, we ensure that the AI never comes into contact with your actual passwords. Every critical action still requires human approval as the primary safeguard. As a result, ZENITH proves that we can have highly helpful AI without sacrificing privacy. This is a blueprint for future technology that is intelligent, secure, and remains user-centric.

Sering kali kita masih membayangkan kecerdasan buatan (AI) hanya sebatas chatbot. tempat bertanya, mencari jawaban, atau sekadar berdialog. Namun, bagaimana jika AI mampu melampaui peran tersebut dan bertindak sebagai asisten pribadi yang benar-benar mengeksekusi tugas kompleks, mulai dari menerbitkan kode ke repositori GitHub hingga mengatur jadwal dengan menggunakan Google Calendar, tanpa mengorbankan keamanan data penggunanya? Pertanyaan inilah yang menjadi fondasi dari ZENITH: sebuah orkestrator AI generasi baru yang tidak hanya berfokus pada kecerdasan, tetapi juga menempatkan kedaulatan identitas (identity sovereignty) sebagai prinsip utama dalam setiap aksi yang dijalankan.Membangun sebuah sistem AI kompleks seperti ZENITH, yang mampu mengoordinasikan model-model canggih seperti Google Gemini 3.1 Pro, Imagen, dan Veo 3.1 secara simultan, menghadirkan sebuah paradoks fundamental: bagaimana merancang sistem yang cukup kuat untuk “bertindak” (acting) atas nama pengguna, namun tetap cukup aman sehingga tidak pernah menyentuh kredensial paling sensitif milik pengguna? Dalam praktik pengembangan agen AI, kesalahan mendasar yang masih sering ditemui adalah penggunaan metode hardcoding API key atau penyimpanan kredensial di sisi frontend state yang rentan terhadap eksploitasi. Bagi ZENITH, pendekatan tersebut bukanlah sebuah opsi. Oleh karena itu, arsitektur Zero-Trust diterapkan secara menyeluruh, dengan Auth0 Token Vault sebagai fondasi utama dalam pengelolaan identitas dan akses. Pengalaman empiris menunjukkan bahwa kredensial yang terekspos dapat dieksploitasi dalam hitungan detik. Bot otomatis mampu mengonsumsi kuota API secara masif, menegaskan bahwa dalam ekosistem digital modern, akses tidak sah bukan sekadar kemungkinan, melainkan keniscayaan. Menjawab tantangan ini, pengembangan ZENITH berfokus pada implementasi Secure Handshake Protocol sebagai mekanisme kontrol utama.Alih-alih bertindak sebagai perantara yang menyimpan kunci akses, ZENITH memanfaatkan Auth0 untuk membangun lapisan Token Vault yang terisolasi. Dalam skenario praktis, misalnya ketika pengguna meminta sistem untuk membuat dan mempublikasikan konten ke repositori GitHub, proses tidak langsung dieksekusi. Sebaliknya, sistem memicu serangkaian secure handshake sebagai berikut:<ol> <li style="text-align: justify">Pengguna memverifikasi identitas melalui Multi-Factor Authentication (MFA).</li> <li style="text-align: justify">Permintaan dievaluasi menggunakan kebijakan akses granular (fine-grained policies) berbasis OpenFGA.</li> <li style="text-align: justify">Token Vault hanya menerbitkan scoped token berdurasi pendek yang spesifik untuk aksi tersebut.</li> </ol>Salah satu komponen paling kritis dalam pendekatan ini adalah implementasi MFA step-up secara real-time menggunakan Google Authenticator. Mekanisme ini bukan sekadar fitur tambahan, melainkan titik kontrol di mana “aksi agentik” benar-benar berada di bawah verifikasi manusia. Sebelum ZENITH mengeksekusi operasi sensitif, pengguna harus membuktikan kehadirannya secara real-time, melampaui sekadar validitas sesi. Dengan pendekatan ini, large language model (LLM) tidak pernah memiliki akses langsung terhadap kredensial asli pengguna. Dengan memanfaatkan protokol Universal Login dan OpenID Connect (OIDC) dari Auth0, ZENITH tidak pernah mengakses raw secrets. Sebagai gantinya, sistem hanya menerima Action Token yang bersifat sementara dan terikat secara kriptografis pada identitas terverifikasi. Setiap “Neural Request”, baik itu sintesis gambar resolusi tinggi maupun produksi video sinematik, memiliki keterkaitan yang jelas dengan identitas pengguna yang sah. Integrasi Auth0 dalam konteks ini melampaui sekadar fitur autentikasi. Ia berfungsi sebagai sebuah Governance Layer, di mana identitas menjadi perimeter utama dalam arsitektur sistem. Tanpa autentikasi melalui Token Vault, agen-agen AI seperti Copywriter, Illustrator, dan Animator secara sistemik tidak dapat diinisialisasi.Pendekatan dekopling (pemisahan) antara agensi AI dan identitas ini memungkinkan fokus pengembangan diarahkan pada kompleksitas orkestrasi model multi-modal, tanpa mengorbankan aspek keamanan. Ketika Auth0 menangani manajemen sesi dan pertukaran token secara aman, ZENITH memiliki ruang untuk mengeksplorasi kapabilitas model terbaru seperti Gemini 3.1 Pro secara optimal. Proyek ini menunjukkan bahwa generasi AI berikutnya tidak hanya ditentukan oleh kualitas prompt engineering, tetapi juga oleh kekuatan infrastruktur keamanannya. Melalui integrasi Auth0 Token Vault dan OpenFGA, ZENITH bertransformasi dari sekadar demonstrasi teknologi menjadi sebuah Authorized Agent yang siap untuk diimplementasikan pada lingkungan produksi.Kita sedang memasuki era di mana AI tidak hanya menghasilkan rekomendasi, tetapi juga melakukan tindakan nyata, baik di ranah digital maupun fisik. Dalam konteks tersebut, kepercayaan (trust) menjadi mata uang utama. Auth0 menyediakan fondasi keamanannya, sementara ZENITH menghadirkan visinya. Keduanya merepresentasikan cetak biru bagi sistem AI yang tidak hanya canggih, tetapi juga aman dan tetap berpusat pada manusia.References Mensah, Frank. (2024). Zero Trust Architecture: A Comprehensive Review of Principles, Implementation Strategies, and Future Directions in Enterprise Cybersecurity. International Journal of Academic and Industrial Research Innovations (IJAIRI). 10. 339-346 Ejiofor, Oluomachi &amp; Olusoga, Oluwafemi &amp; Akinsola, Ahmed. (2025). Zero trust architecture: A paradigm shift in network security. Computer Science &amp; IT Research Journal. 6. 104-124. 10.51594/csitrj.v6i3.1871 Ume Nisa, Muhammad Shirazi, Mohamed Ali Saip, Muhammad Syafiq Mohd Pozi, Agentic AI: The age of reasoning. A review, Journal of Automation and Intelligence, Volume 5, Issue 1, 2026, Pages 69-89, ISSN 2949-8554, https://doi.org/10.1016/j.jai.2025.08.003 Zhou, Qian &amp; Elbadry, Mohammed &amp; ye, Fan &amp; Yang, Yuanyuan. (2020). Towards Fine-Grained Access Control in Enterprise-Scale Internet-of-Things. IEEE Transactions on Mobile Computing. PP. 1-1. 10.1109/TMC.2020.2984700 Wissuchek, Christopher &amp; Zschech, Patrick. (2025). Challenges in Managing the Relationship Between Agentic AI Systems and Humans in Organizations. 10.1007/978-3-031-94193-1_1 Khalid, Omer &amp; Farooqi, Ammad &amp; Bilal, Muhammad. (2025). Agentic AI: A Review, Applications, and Open Research Challenges. 10.20944/preprints202512.0592.v1 Ranjan Sapkota, Konstantinos I. Roumeliotis, Manoj Karkee, AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges, Information Fusion, Volume 126, Part B, 2026, 103599, ISSN 1566-2535, https://doi.org/10.1016/j.inffus.2025.103599 Wu, Yuhao &amp; Yang, Ke &amp; Roesner, Franziska &amp; Kohno, Tadayoshi &amp; Zhang, Ning &amp; Iqbal, Umar. (2025). Towards Automating Data Access Permissions in AI Agents. 10.48550/arXiv.2511.17959 Batool, Amna &amp; Zowghi, Didar &amp; Bano, Muneera. (2024). AI Governance: A Systematic Literature Review. 10.21203/rs.3.rs-4784792/v1 Akhmad, K. (2026). Zenith: An Agentic Governance Framework with Sovereign AI and Fine-Grained Authorization (V1.0.0). Zenodo. https://doi.org/10.5281/zenodo.19467804

We often still think of artificial intelligence (AI) as nothing more than a chatbot—a place to ask questions, find answers, or simply have a conversation. But what if AI could go beyond that role and act as a personal assistant that actually performs complex tasks, from pushing code to a GitHub repository to managing schedules using Google Calendar, without compromising user data security? This question forms the foundation of ZENITH: a next-generation AI orchestrator that focuses not only on intelligence but also prioritizes identity sovereignty as a core principle in every action it performs.Building a complex AI system like ZENITH, capable of simultaneously coordinating advanced models such as Google Gemini 3.1 Pro, Imagen, and Veo 3.1, presents a fundamental paradox: how to design a system powerful enough to “act” on behalf of the user, yet secure enough that it never touches the user’s most sensitive credentials? In AI agent development, a common mistake still frequently encountered is the use of hardcoded API keys or storing credentials in frontend state—both of which are vulnerable to exploitation. For ZENITH, such an approach is not an option. Therefore, a Zero-Trust architecture is implemented comprehensively, with Auth0 Token Vault serving as the primary foundation for identity and access management. Empirical experience shows that exposed credentials can be exploited in a matter of seconds. Automated bots are capable of massively consuming API quotas, underscoring that in modern digital ecosystems, unauthorized access is not merely a possibility but an inevitability. To address this challenge, ZENITH’s development focuses on implementing the Secure Handshake Protocol as the primary control mechanism.Instead of acting as an intermediary that stores access keys, ZENITH leverages Auth0 to build an isolated Token Vault layer. In a practical scenario, for example, when a user requests the system to create and publish content to a GitHub repository, the process is not executed directly. Instead, the system triggers a series of secure handshakes as follows:<ol> <li style="text-align: justify">The user verifies their identity via Multi-Factor Authentication (MFA).</li> <li style="text-align: justify">The request is evaluated using fine-grained access policies based on OpenFGA.</li> <li style="text-align: justify">The Token Vault issues only short-lived, scoped tokens specific to that action.</li> </ol>One of the most critical components of this approach is the real-time implementation of step-up MFA using Google Authenticator. This mechanism is not merely an additional feature, but a control point where “agent actions” are truly subject to human verification. Before ZENITH executes sensitive operations, the user must prove their presence in real-time, going beyond mere session validity. With this approach, the large language model (LLM) never has direct access to the user’s original credentials. By leveraging Auth0’s Universal Login and OpenID Connect (OIDC) protocols, ZENITH never accesses raw secrets. Instead, the system only receives temporary Action Tokens cryptographically bound to a verified identity. Every “Neural Request”—whether it’s high-resolution image synthesis or cinematic video production—has a clear link to the user’s legitimate identity. Auth0 integration in this context goes beyond mere authentication features. It functions as a Governance Layer, where identity serves as the primary perimeter within the system architecture. Without authentication via the Token Vault, AI agents such as Copywriter, Illustrator, and Animator cannot be systematically initialized.This decoupling approach between AI agents and identity allows development focus to be directed toward the complexity of multi-modal model orchestration, without compromising security. While Auth0 handles session management and secure token exchange, ZENITH has the freedom to optimally explore the capabilities of the latest models, such as Gemini 3.1 Pro. This project demonstrates that the next generation of AI is not only defined by the quality of prompt engineering but also by the strength of its security infrastructure. Through the integration of Auth0 Token Vault and OpenFGA, ZENITH has transformed from a mere technology demonstration into an Authorized Agent ready for deployment in a production environment.We are entering an era where AI not only generates recommendations but also takes tangible actions, both in the digital and physical realms. In this context, trust becomes the primary currency. Auth0 provides the security foundation, while ZENITH brings its vision to life. Together, they represent a blueprint for AI systems that are not only advanced but also secure and human-centered.References Mensah, Frank. (2024). Zero Trust Architecture: A Comprehensive Review of Principles, Implementation Strategies, and Future Directions in Enterprise Cybersecurity. International Journal of Academic and Industrial Research Innovations (IJAIRI). 10. 339-346 Ejiofor, Oluomachi &amp; Olusoga, Oluwafemi &amp; Akinsola, Ahmed. (2025). Zero Trust Architecture: A Paradigm Shift in Network Security. Computer Science &amp; IT Research Journal. 6. 104–124. 10.51594/csitrj.v6i3.1871 Ume Nisa, Muhammad Shirazi, Mohamed Ali Saip, Muhammad Syafiq Mohd Pozi, Agentic AI: The Age of Reasoning. A review, Journal of Automation and Intelligence, Volume 5, Issue 1, 2026, Pages 69–89, ISSN 2949-8554, https://doi.org/10.1016/j.jai.2025.08.003 Zhou, Qian &amp; Elbadry, Mohammed &amp; Ye, Fan &amp; Yang, Yuanyuan. (2020). Towards Fine-Grained Access Control in Enterprise-Scale Internet-of-Things. IEEE Transactions on Mobile Computing. pp. 1–1. 10.1109/TMC.2020.2984700 Wissuchek, Christopher &amp; Zschech, Patrick. (2025). Challenges in Managing the Relationship Between Agentic AI Systems and Humans in Organizations. 10.1007/978-3-031-94193-1_1 Khalid, Omer &amp; Farooqi, Ammad &amp; Bilal, Muhammad. (2025). Agentic AI: A Review, Applications, and Open Research Challenges. 10.20944/preprints202512.0592.v1 Ranjan Sapkota, Konstantinos I. Roumeliotis, Manoj Karkee, AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications, and Challenges, Information Fusion, Volume 126, Part B, 2026, 103599, ISSN 1566-2535, https://doi.org/10.1016/j.inffus.2025.103599 Wu, Yuhao &amp; Yang, Ke &amp; Roesner, Franziska &amp; Kohno, Tadayoshi &amp; Zhang, Ning &amp; Iqbal, Umar. (2025). Towards Automating Data Access Permissions in AI Agents. 10.48550/arXiv.2511.17959 Batool, Amna &amp; Zowghi, Didar &amp; Bano, Muneera. (2024). AI Governance: A Systematic Literature Review. 10.21203/rs.3.rs-4784792/v1 Akhmad, K. (2026). Zenith: An Agentic Governance Framework with Sovereign AI and Fine-Grained Authorization (V1.0.0). Zenodo. https://doi.org/10.5281/zenodo.19467804

Dekopling Agensi dan Identitas: Orkestrasi AI Berbasis Arsitektur Zero-Trust

Decoupling Agency and Identity: AI Orchestration Based on a Zero-Trust Architecture

The rapid evolution of Internet of Things (IoT) systems, smart cities, and intelligent infrastructures is reshaping networking and cybersecurity requirements. Traditional architectures struggle to meet the demands of scalability, adaptability, and security in dynamic environments. This paper reviews recent advances in AI-driven networking, edge intelligence, and modern cybersecurity paradigms, including zero-trust and AI-based threat detection. It also examines proactive congestion control mechanisms for maintaining Quality of Service (QoS). By integrating artificial intelligence with distributed computing and adaptive security frameworks, next-generation networks can achieve improved reliability, efficiency, and resilience.

1. IntroductionThe increasing deployment of IoT devices and real-time applications has introduced significant challenges in modern communication networks. Smart city services, intelligent transportation systems, and critical infrastructures require low latency, high reliability, and strong security guarantees. Traditional approaches based on static routing and perimeter-based security are insufficient in such dynamic and heterogeneous environments [1], [2]. Recent developments are driving a transition toward intelligent, decentralized network architectures, where Artificial Intelligence (AI), edge computing, and adaptive security mechanisms operate jointly. These systems enable real-time decision-making, predictive analysis, and automated response to network conditions.2. AI-Driven Networking2.1 Intelligent RoutingAI-based routing leverages reinforcement learning (RL) and deep learning to dynamically select forwarding paths based on observed network conditions. A typical RL-based routing model defines:• State: link quality, queue length, delay, packet loss• Action: next-hop selection• Reward: function of latency, delivery ratio, and congestionSuch formulations enable continuous learning and adaptation to changing traffic patterns. Recent studies demonstrate that Q-learning-based next-hop selection improves packet delivery ratio and reduces end-to-end delay in dynamic IoT environments [11].2.2 Predictive Network ManagementAI-driven predictive models analyze temporal traffic patterns to anticipate congestion and link degradation. For example, time-series and deep learning models can forecast traffic load at edge nodes, enabling preemptive traffic rerouting and load balancing [3]. This shifts network control from reactive to proactive operation.3. Edge and Fog ComputingEdge and fog computing extend processing capabilities closer to data sources, reducing reliance on centralized cloud infrastructures. Key advantages include Reduced end-to-end latency, Localized processing of time-sensitive data, Reduced backbone traffic, and Enhanced privacy through local enforcement. The integration of Edge AI allows inference tasks (e.g., anomaly detection, traffic prediction) to be executed directly at gateways or edge servers, enabling near real-time decision-making [4], [5]. Figure 1: Multi-layer architecture illustrating IoT devices, edge gateways, fog nodes, and cloud platforms supporting real-time processing and secure communication.The architecture in Figure 1 illustrates hierarchical data processing: sensor data is first handled at the edge (filtering, aggregation), then processed at fog nodes (analytics, coordination), and finally stored or globally optimized in the cloud. This distribution reduces latency and improves scalability.4. Cybersecurity in Modern Networks4.1 Zero-Trust ArchitectureZero-Trust Architecture (ZTA) enforces continuous verification of users and devices, independent of network location. Access decisions are based on identity, device posture, and contextual policies, reducing the attack surface in distributed systems [6].4.2 AI-Based Threat DetectionMachine learning models can identify anomalous traffic patterns by analyzing features such as flow statistics, packet timing, and protocol behavior. These systems enable detection of zero-day attacks. identification of lateral movement, and automated incident response.Recent approaches combine deep learning with edge deployment to achieve low-latency threat detection [7].4.3 Emerging Security ParadigmsEmerging approaches such as semantic communications aim to transmit only task-relevant information rather than raw data, improving both efficiency and security [8]. Additionally, blockchain-based mechanisms are being explored for decentralized identity and trust management.5. Congestion Control and Traffic EngineeringEfficient traffic management is essential for maintaining QoS in large-scale networks.Traditional congestion control mechanisms rely on packet loss or delay as implicit signals, leading to delayed response. In contrast, modern approaches emphasize explicit congestion signaling, queue-aware routing decisions, and cross-layer optimization. For example, congestion-aware routing can incorporate queue length or buffer occupancy into routing decisions. Proactive mechanisms, such as early congestion notification, allow upstream nodes to adjust transmission rates before packet loss occurs [10], [11]. When combined with AI-based prediction models, these mechanisms enable anticipatory congestion control, improving throughput stability and reducing latency variability [9].6. Integration of Emerging TechnologiesTable 1: Key Technologies in Future Networking and Cybersecurity<table> <tbody> <tr> <td>Technology</td> <td>Networking Role</td> <td>Security Contribution</td> </tr> <tr> <td>AI / ML / LLMs</td> <td>Adaptive Routing, Prediction</td> <td>Intelligent anomaly detection</td> </tr> <tr> <td>Edge AI</td> <td>Real-time local processing</td> <td>Low-latency threat detection</td> </tr> <tr> <td>Zero-Trust</td> <td>Access Control</td> <td>Continuous Authentication</td> </tr> <tr> <td>Blockchain</td> <td>DIstributed Coordination</td> <td>Data Integrity</td> </tr> <tr> <td>AI-based Congestion Control</td> <td>Traffic Optimization</td> <td>Mitigates overload conditions</td> </tr> </tbody> </table>The integration of these technologies presented in Table 1 enables coordinated operation across networking and security layers. For example, edge-based anomaly detection can inform routing decisions, while congestion signals can be used to prioritize critical traffic.7. Challenges and Future DirectionsDespite recent progress, several challenges remain:<ul> <li style="text-align: justify">Scalability: AI models must operate efficiently across thousands of distributed nodes</li> <li style="text-align: justify">Resource constraints: IoT devices have limited computation and energy capacity</li> <li style="text-align: justify">Data privacy: centralized learning raises privacy concerns</li> <li style="text-align: justify">Interoperability: integration across heterogeneous systems remains complexPromising research directions include lightweight and distributed AI models, federated learning for privacy preservation, self-healing and autonomous networks, joint optimization of routing, congestion control, and security.</li> </ul>8. ConclusionThe convergence of AI-driven networking, edge intelligence, and adaptive cybersecurity frameworks is shaping the future of communication systems. Intelligent routing, predictive management, and proactive congestion control provide a foundation for scalable and resilient networks. These advancements are critical for supporting the growing demands of smart systems and ensuring secure and efficient network operation.References[1] H. Du et al., “The age of generative AI and AI-generated everything,” IEEE Netw., vol. 38, no. 6, pp. 501–512, Nov./Dec. 2024, doi: 10.1109/MNET.2024.3422241.[2] Y. Huang et al., “Large language models for networking: Applications, enabling techniques, and challenges,” IEEE Netw., vol. 39, no. 1, pp. 235–242, Jan./Feb. 2024.[3] R. Sun et al., “Knowledge-driven deep learning paradigms for wireless network optimization in 6G,” IEEE Netw., vol. 38, no. 2, pp. 70–78, Mar./Apr. 2024.[4] S. S. Gill et al., “Edge AI: A taxonomy, systematic review and future directions,” Cluster Comput., vol. 28, Art. no. 18, 2025.[5] C. Wang et al., “The security and privacy of mobile edge computing: An artificial intelligence perspective,” arXiv preprint arXiv:2401.01589, 2024.[6] NIST, Zero Trust Architecture, SP 800-207, Gaithersburg, MD, USA, Aug. 2020.Networks, IEEE Std 802.1Q-2024, 2024.[7] Y. Wang et al., “Advanced persistent threat detection,” IEEE Netw., 2024.[8] Z. Yang et al., “Secure semantic communications,” IEEE Netw., 2024.[9] H. Zhang et al., “Deep learning-based traffic prediction and congestion control for smart cities,” IEEE Netw., vol. 35, no. 2, pp. 198–204, Mar./Apr. 2021.Malaysia, 2010.[10] M. M. Kadhum, “Enhancing the performance and reliability of IoT systems using predictive route recovery mechanism,” in Proc. IEEE TENCON, Dec. 2024, pp. 489–493.[11] M. M. Kadhum, “Machine learning technique for route discovery in dynamic Internet of Things environments: A theoretical perspective,” in Proc. Int. Conf. Sens. Technol. (ICST), Dec. 2024, pp. 1–4.

Volume 1, Issue 2 (2026)

Articles in This Issue