𝗧𝗵𝗲 𝗙𝘂𝗹𝗹𝗔𝗴𝗲𝗻𝘁𝗶𝗰𝗦𝘁𝗮𝗰𝗸 𝗠𝗮𝗻𝗶𝗳𝗲𝘀𝘁𝗼

Everyone is trying to build agents.

People talk about prompts, tools, and LLMs. They focus on agents calling APIs. This is only the start.

In a few years, the question will change. You will not ask how to build an agent. You will ask how to build reliable systems made of agents.

An agent is not a system. An agent is one part of a larger architecture.

The next phase of software requires infrastructure. Agents, humans, and services must interact with trust, memory, and proof. I call this the FullAgenticStack.

Building an agent is getting easier. This creates a trap. People think agentic software is just an LLM plus tools and memory.

That is not enough.

A chatbot with tool calling is not a system. A chain of prompts is not an architecture.

The real problem is not making an agent do a task once. The real problem is making systems of agents work safely and repeatedly in the real world.

You must answer these questions:

  • How do you orchestrate agents?
  • How do you authenticate them?
  • How do you audit their actions?
  • How do you limit their permissions?
  • How do you recover state after a failure?
  • How do you prevent chaos when agents coordinate?

These are not small problems. They are the foundation.

The FullAgenticStack provides the environment for agentic software to work in production. It includes:

  • Human and agent identity
  • Authentication and authorization
  • Permissions and capabilities
  • State and memory
  • Event history and observability
  • Recovery and rollback
  • Proof of execution
  • Zero-trust interaction

Tool calling is just an interface. It does not solve identity. It does not solve responsibility.

If an agent buys a product, who authorized it? If an agent fails, how do you recover? If an agent causes harm, how do you prove what happened?

These are engineering requirements.

The next web will consist of agents acting for people and companies. Agents will negotiate, schedule, buy, and sell.

For this to work, agents cannot be invisible scripts. They must be identifiable, observable, and provable.

You do not need a prompt stack. You need a FullAgenticStack.

Stop looking at the surface. The goal is not whether an agent can call a tool. The goal is whether an agent can exist in a trustworthy system.

Agents are not the end. They are the beginning of a new architecture.

Manifesto The FullAgenticStack: Agen bukan sekadar LLM

Era wrapper LLM sederhana akan segera berakhir. Kita sedang bergerak menuju era Agentic Workflows.

LLM adalah sebuah model. Agen adalah sebuah sistem.

Apa itu Agen?

LLM adalah mesin penggeraknya, tetapi agen adalah kendaraannya. LLM menyediakan kemampuan penalaran, tetapi tanpa komponen pendukung lainnya, ia tidak dapat bertindak secara otonom di dunia nyata.

The FullAgenticStack

1. Otak (The Brain - LLM)

LLM berfungsi sebagai pusat kendali. Ia melakukan penalaran, memahami instruksi, dan memutuskan langkah selanjutnya.

2. Memori (Memory)

Agen membutuhkan memori untuk mempertahankan konteks dan pembelajaran.

  • Memori Jangka Pendek: Menggunakan context window untuk mempertahankan alur percakapan saat ini.
  • Memori Jangka Panjang: Menggunakan vector databases untuk menyimpan dan mengambil informasi dari interaksi masa lalu.

3. Perencanaan (Planning)

Kemampuan untuk memecah tugas kompleks menjadi langkah-langkah kecil yang dapat dikelola. Ini mencakup:

  • Dekomposisi Tugas: Memecah tujuan besar menjadi sub-tugas yang lebih sederhana.
  • Refleksi (Self-Correction): Mengevaluasi hasil kerja sendiri dan memperbaiki kesalahan secara mandiri.

4. Alat (Tools)

Agar agen dapat berinteraksi dengan dunia luar, ia memerlukan alat. Ini bisa berupa:

  • Akses ke API.
  • Browser web.
  • Code interpreter.

5. Lingkungan (Environment)

Tempat di mana agen beroperasi dan mengeksekusi tindakannya. Ini sering kali berupa sandbox yang aman untuk menjalankan kode atau berinteraksi dengan sistem eksternal.

Kesimpulan

Membangun agen yang tangguh bukan hanya tentang memilih LLM terbaik, melainkan tentang membangun stack yang lengkap dan terintegrasi.

Optional learning community: https://t.me/GyaanSetuAi