Membangun Agen Terkelola

Agen terkelola di Gemini API memungkinkan Anda memperluas agen Antigravity dengan petunjuk, keterampilan, dan data Anda sendiri. Anda dapat menyesuaikan agen secara inline saat interaksi, atau menyimpan konfigurasi sebagai agen terkelola yang Anda panggil berdasarkan ID.

Menyesuaikan agen Antigravity

Cara tercepat untuk membuat agen kustom adalah dengan meneruskan konfigurasi Anda secara inline saat membuat interaksi baru tanpa langkah pendaftaran yang diperlukan. Anda dapat memperluas agen dengan tiga cara:

  • Petunjuk sistem: Teruskan teks inline melalui system_instruction untuk membentuk perilaku.
  • Alat: Ganti alat default (Eksekusi Kode, Penelusuran, Konteks URL), daftarkan server MCP jarak jauh, atau tentukan fungsi kustom (Panggilan Fungsi).
  • File dan keterampilan: Pasang file seperti AGENTS.md dan SKILL.md ke dalam lingkungan.

Berikut adalah contoh meneruskan ketiganya secara inline:

Python

from google import genai

client = genai.Client()

interaction = client.interactions.create(
    agent="antigravity-preview-05-2026",
    input="Analyze the Q1 revenue data and create a slide deck.",
    system_instruction="You are a data analyst. Always include visualizations and export results as PDF.",        
    environment={
        "type": "remote",
        "sources": [
            {
                "type": "inline",
                "target": ".agents/AGENTS.md",
                "content": "Always use matplotlib for charts. Include a summary table in every report.",
            },
            {
                "type": "inline",
                "target": ".agents/skills/slide-maker/SKILL.md",
                "content": "---\nname: slide-maker\n---\n# Slide Maker\nCreate HTML slide decks from data analysis results.",
            },
        ],
    },
)

print(interaction.output_text)

JavaScript

import { GoogleGenAI } from "@google/genai";

const client = new GoogleGenAI({});

const interaction = await client.interactions.create({
    agent: "antigravity-preview-05-2026",
    input: "Analyze the Q1 revenue data and create a slide deck.",
    system_instruction: "You are a data analyst. Always include visualizations and export results as PDF.",        
    environment: {
        type: "remote",
        sources: [
            {
                type: "inline",
                target: ".agents/AGENTS.md",
                content: "Always use matplotlib for charts. Include a summary table in every report.",
            },
            {
                type: "inline",
                target: ".agents/skills/slide-maker/SKILL.md",
                content: "---\nname: slide-maker\n---\n# Slide Maker\nCreate HTML slide decks from data analysis results.",
            },
        ],
    },
}, { timeout: 300000 });

console.log(interaction.output_text);

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-d '{
    "agent": "antigravity-preview-05-2026",
    "input": "Analyze the Q1 revenue data and create a slide deck.",
    "system_instruction": "You are a data analyst. Always include visualizations and export results as PDF.",
    "environment": {
        "type": "remote",
        "sources": [
            {
                "type": "inline",
                "target": ".agents/AGENTS.md",
                "content": "Always use matplotlib for charts. Include a summary table in every report."
            },
            {
                "type": "inline",
                "target": ".agents/skills/slide-maker/SKILL.md",
                "content": "---\nname: slide-maker\n---\n# Slide Maker\nCreate HTML slide decks from data analysis results."
            }
        ]
    }
}'

Semuanya ditentukan saat interaksi. Tidak perlu mendaftarkan apa pun terlebih dahulu. Harness agen Antigravity menyediakan runtime (eksekusi kode, pengelolaan file, akses web) dan lapisan konfigurasi Anda di atasnya.

Alat dan petunjuk sistem

Anda dapat menyesuaikan perilaku dan kemampuan agen untuk interaksi tertentu menggunakan parameter system_instruction dan tools.

  • Petunjuk sistem: Gunakan parameter system_instruction untuk meneruskan teks inline yang membentuk perilaku agen. Hal ini ideal untuk penyesuaian cepat yang ingin Anda ubah per panggilan. system_instruction dan AGENTS.md bersifat aditif; keduanya berlaku jika ada.
  • Alat: Secara default, agen Antigravity memiliki akses ke code_execution, google_search, dan url_context. Anda dapat mengganti daftar ini dengan meneruskan parameter tools saat interaksi. Anda juga dapat mendaftarkan server MCP jarak jauh atau menentukan fungsi kustom (panggilan fungsi) untuk menghubungkan agen ke API dan database Anda sendiri. Untuk mengetahui detail lengkap tentang alat yang tersedia, lihat Agen Antigravity: Alat yang didukung.

Penyesuaian berbasis file

Struktur direktori agen

Meskipun Anda dapat meneruskan konfigurasi secara inline, sebaiknya atur file agen Anda dalam direktori terstruktur. Hal ini akan memudahkan pengelolaan, kontrol versi, dan pemasangan ke lingkungan agen.

Direktori project agen standar terlihat seperti ini:

my-agent/
├── AGENTS.md        # Instructions on how the agent should operate
├── skills/          # Custom skills (subfolders and SKILL.md files)
│   └── slide-maker/
│       └── SKILL.md
└── workspace/       # Initial data files and knowledge

Runtime Antigravity memindai .agents/ (dan root lingkungan) untuk file ini.

AGENTS.md

Agen otomatis memuat .agents/AGENTS.md (atau /.agents/AGENTS.md) dari lingkungan sebagai petunjuk sistem saat startup. Gunakan AGENTS.md untuk definisi persona bentuk panjang, panduan mendetail, dan petunjuk yang ingin Anda kontrol versinya bersama kode Anda.

Pasang AGENTS.md menggunakan sumber inline:

Python

from google import genai

client = genai.Client()

interaction = client.interactions.create(
    agent="antigravity-preview-05-2026",
    input="Analyze the Q1 revenue data and create a report.",
    system_instruction="You are a data analyst. Always include visualizations and export results as PDF.",
    environment={
        "type": "remote",
        "sources": [
            {
                "type": "inline",
                "target": ".agents/AGENTS.md",
                "content": "Always use matplotlib for charts. Include a summary table in every report.",
            },
        ],
    },
)

print(interaction.output_text)

JavaScript

import { GoogleGenAI } from "@google/genai";

const client = new GoogleGenAI({});

const interaction = await client.interactions.create({
    agent: "antigravity-preview-05-2026",
    input: "Analyze the Q1 revenue data and create a report.",
    system_instruction: "You are a data analyst. Always include visualizations and export results as PDF.",
    environment: {
        type: "remote",
        sources: [
            {
                type: "inline",
                target: ".agents/AGENTS.md",
                content: "Always use matplotlib for charts. Include a summary table in every report.",
            },
        ],
    },
}, { timeout: 300000 });

console.log(interaction.output_text);

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "Content-Type: application/json" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -d '{
      "agent": "antigravity-preview-05-2026",
      "input": "Analyze the Q1 revenue data and create a report.",
      "system_instruction": "You are a data analyst. Always include visualizations and export results as PDF.",
      "environment": {
          "type": "remote",
          "sources": [
              {
                  "type": "inline",
                  "target": ".agents/AGENTS.md",
                  "content": "Always use matplotlib for charts. Include a summary table in every report."
              }
          ]
      }
  }'

Keterampilan: SKILL.md

Keterampilan adalah file yang memperluas kemampuan agen. Tempatkan file tersebut di bagian .agents/skills/<skill-name>/SKILL.md dan harness akan otomatis menemukan serta mendaftarkannya.

.agents/
├── AGENTS.md
└── skills/
    └── slide-maker/
        └── SKILL.md

Pasang keterampilan menggunakan sumber inline:

Python

from google import genai

client = genai.Client()

interaction = client.interactions.create(
    agent="antigravity-preview-05-2026",
    input="Create a presentation about our Q1 results.",
    system_instruction="You create presentations from data.",
    environment={
        "type": "remote",
        "sources": [
            {
                "type": "inline",
                "target": ".agents/skills/slide-maker/SKILL.md",
                "content": "---\nname: slide-maker\ndescription: Create HTML slide decks\n---\n# Slide Maker\n\nWhen asked to create a presentation:\n1. Analyze the input data\n2. Create an HTML slide deck with reveal.js\n3. Save to /workspace/output/slides.html",
            },
        ],
    },
)

print(interaction.output_text)

JavaScript

import { GoogleGenAI } from "@google/genai";

const client = new GoogleGenAI({});

const interaction = await client.interactions.create({
    agent: "antigravity-preview-05-2026",
    input: "Create a presentation about our Q1 results.",
    system_instruction: "You create presentations from data.",
    environment: {
        type: "remote",
        sources: [
            {
                type: "inline",
                target: ".agents/skills/slide-maker/SKILL.md",
                content: "---\nname: slide-maker\ndescription: Create HTML slide decks\n---\n# Slide Maker\n\nWhen asked to create a presentation:\n1. Analyze the input data\n2. Create an HTML slide deck with reveal.js\n3. Save to /workspace/output/slides.html",
            },
        ],
    },
}, { timeout: 300000 });

console.log(interaction.output_text);

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "Content-Type: application/json" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -d '{
      "agent": "antigravity-preview-05-2026",
      "input": "Create a presentation about our Q1 results.",
      "system_instruction": "You create presentations from data.",
      "environment": {
          "type": "remote",
          "sources": [
              {
                  "type": "inline",
                  "target": ".agents/skills/slide-maker/SKILL.md",
                  "content": "---\nname: slide-maker\ndescription: Create HTML slide decks\n---\n# Slide Maker\n\nWhen asked to create a presentation:\n1. Analyze the input data\n2. Create an HTML slide deck with reveal.js\n3. Save to /workspace/output/slides.html"
              }
          ]
      }
  }'

Keterampilan yang dimuat dari .agents/skills/ dan /.agents/skills/ akan otomatis ditemukan.

Membuat agen terkelola

Setelah melakukan iterasi pada konfigurasi, Anda dapat membuatnya sebagai agen terkelola dengan agents.create. Hal ini memungkinkan Anda memanggil agen berdasarkan ID tanpa mengulangi konfigurasi setiap kali.

Dari sumber

Tentukan base_agent, id, system_instruction, dan base_environment dengan sumber. Platform ini menyediakan sandbox baru dengan file Anda pada setiap pemanggilan. Lihat Lingkungan untuk mengetahui jenis sumber yang tersedia (Git, GCS, inline).

Python

from google import genai

client = genai.Client()

agent = client.agents.create(
    id="data-analyst",
    base_agent="antigravity-preview-05-2026",
    system_instruction="You are a data analyst. Always include visualizations and export results as PDF.",
    base_environment={
        "type": "remote",
        "sources": [
            {
                "type": "inline",
                "target": ".agents/AGENTS.md",
                "content": "Always use matplotlib for charts. Include a summary table in every report.",
            },
            {
                "type": "inline",
                "target": ".agents/skills/slide-maker/SKILL.md",
                "content": "---\nname: slide-maker\n---\n# Slide Maker\nCreate HTML slide decks from data analysis results.",
            },
            {
                "type": "repository",
                "source": "https://github.com/my-org/analysis-templates",
                "target": "/workspace/templates",
            },
        ],
    },
)

print(f"Created agent: {agent.id}")

JavaScript

import { GoogleGenAI } from "@google/genai";

const client = new GoogleGenAI({});

const agent = await client.agents.create({
    id: "data-analyst",
    base_agent: "antigravity-preview-05-2026",
    system_instruction: "You are a data analyst. Always include visualizations and export results as PDF.",
    base_environment: {
        type: "remote",
        sources: [
            {
                type: "inline",
                target: ".agents/AGENTS.md",
                content: "Always use matplotlib for charts. Include a summary table in every report.",
            },
            {
                type: "inline",
                target: ".agents/skills/slide-maker/SKILL.md",
                content: "---\nname: slide-maker\n---\n# Slide Maker\nCreate HTML slide decks from data analysis results.",
            },
            {
                type: "repository",
                source: "https://github.com/my-org/analysis-templates",
                target: "/workspace/templates",
            },
        ],
    },
});

console.log(`Created agent: ${agent.id}`);

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/agents" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-d '{
    "id": "data-analyst",
    "base_agent": "antigravity-preview-05-2026",
    "system_instruction": "You are a data analyst. Always include visualizations and export results as PDF.",
    "base_environment": {
        "type": "remote",
        "sources": [
            {
                "type": "inline",
                "target": ".agents/AGENTS.md",
                "content": "Always use matplotlib for charts. Include a summary table in every report."
            },
            {
                "type": "inline",
                "target": ".agents/skills/slide-maker/SKILL.md",
                "content": "---\nname: slide-maker\n---\n# Slide Maker\nCreate HTML slide decks from data analysis results."
            },
            {
                "type": "repository",
                "source": "https://github.com/my-org/analysis-templates",
                "target": "/workspace/templates"
            }
        ]
    }
}'

Dari lingkungan yang ada (fork)

Lakukan iterasi dengan agen Antigravity dasar hingga lingkungan sesuai (paket terinstal, file ada), lalu fork ke agen terkelola.

Python

from google import genai

client = genai.Client()

# Step 1: set up the environment interactively
interaction = client.interactions.create(
    agent="antigravity-preview-05-2026",
    input="Install pandas, matplotlib, and seaborn. Create an analysis template at /workspace/template.py.",
    environment="remote",
)

# Step 2: fork that environment into a managed agent

agent = client.agents.create(
    id="my-data-analyst",
    base_agent="antigravity-preview-05-2026",
    system_instruction="You are a data analyst. Use the template at /workspace/template.py for all reports.",
    base_environment=interaction.environment_id,
)

print(f"Forked agent successfully: {agent.id}")

JavaScript

import { GoogleGenAI } from "@google/genai";

const client = new GoogleGenAI({});

const interaction = await client.interactions.create({
    agent: "antigravity-preview-05-2026",
    input: "Install pandas, matplotlib, and seaborn. Create an analysis template at /workspace/template.py.",
    environment: "remote",
}, { timeout: 300000 });

const agent = await client.agents.create({
    id: "my-data-analyst",
    base_agent: "antigravity-preview-05-2026",
    system_instruction: "You are a data analyst. Use the template at /workspace/template.py for all reports.",
    base_environment: interaction.environment_id,
});

console.log(`Forked agent successfully: ${agent.id}`);

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "Content-Type: application/json" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -d '{
      "agent": "antigravity-preview-05-2026",
      "input": "Install pandas, matplotlib, and seaborn. Create an analysis template at /workspace/template.py.",
      "environment": "remote"
  }'

Dengan aturan jaringan

Anda dapat mengunci akses keluar atau menyisipkan kredensial saat menyimpan agen terkelola. Untuk mengetahui skema daftar yang diizinkan, pola kredensial, dan karakter pengganti selengkapnya, lihat Lingkungan: Konfigurasi jaringan.

Contoh berikut membuat agen issue-resolver yang hanya dapat mengakses GitHub dan PyPI, dengan kredensial yang disisipkan untuk GitHub:

Python

from google import genai

client = genai.Client()

agent = client.agents.create(
    id="issue-resolver",
    base_agent="antigravity-preview-05-2026",
    system_instruction="You resolve GitHub issues. Clone the repo, find the bug, write the fix, run the tests, and open a PR.",
    base_environment={
        "type": "remote",
        "sources": [
            {
                "type": "repository",
                "source": "https://github.com/my-org/backend",
                "target": "/workspace/repo",
            }
        ],
        "network": {
            "allowlist": [
                {
                    "domain": "api.github.com",
                    "transform": {
                        "Authorization": "Basic YOUR_BASE64_TOKEN"
                    },
                },
                {"domain": "pypi.org"},
            ]
        },
    },
)

print(f"Created issue-resolver agent successfully: {agent.id}")

JavaScript

import { GoogleGenAI } from "@google/genai";

const client = new GoogleGenAI({});

const agent = await client.agents.create({
    id: "issue-resolver",
    base_agent: "antigravity-preview-05-2026",
    system_instruction: "You resolve GitHub issues. Clone the repo, find the bug, write the fix, run the tests, and open a PR.",
    base_environment: {
        type: "remote",
        sources: [
            {
                type: "repository",
                source: "https://github.com/my-org/backend",
                target: "/workspace/repo",
            }
        ],
        network: {
            allowlist: [
                {
                    domain: "api.github.com",
                    transform: {
                        "Authorization": "Basic YOUR_BASE64_TOKEN"
                    },
                },
                { domain: "pypi.org" },
            ]
        }
    },
});

console.log(`Created issue-resolver agent successfully: ${agent.id}`);

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/agents" \
  -H "Content-Type: application/json" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -d '{
      "id": "issue-resolver",
      "base_agent": "antigravity-preview-05-2026",
      "system_instruction": "You resolve GitHub issues. Clone the repo, find the bug, write the fix, run the tests, and open a PR.",
      "base_environment": {
          "type": "remote",
          "sources": [
              {
                  "type": "repository",
                  "source": "https://github.com/my-org/backend",
                  "target": "/workspace/repo"
              }
          ],
          "network": {
              "allowlist": [
                  {
                      "domain": "api.github.com",
                      "transform": {
                          "Authorization": "Basic YOUR_BASE64_TOKEN"
                      }
                  },
                  {"domain": "pypi.org"}
              ]
          }
      }
  }'

Memanggil agen

Panggil agen terkelola Anda dengan ID agen Anda dengan membuat interaksi baru. Setiap pemanggilan akan melakukan fork lingkungan dasar, sehingga setiap operasi dimulai dengan bersih.

Python

result = client.interactions.create(
    agent="data-analyst",
    input="Analyze Q1 revenue data from /workspace/templates/sample.csv and create a slide deck.",
    environment="remote",
)

print(result.output_text)

JavaScript

const result = await client.interactions.create({
    agent: "data-analyst",
    input: "Analyze Q1 revenue data from /workspace/templates/sample.csv and create a slide deck.",
    environment: "remote",
}, { timeout: 300000 });

console.log(result.output_text);

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "Content-Type: application/json" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -d '{
      "agent": "data-analyst",
      "input": "Analyze Q1 revenue data from /workspace/templates/sample.csv and create a slide deck.",
      "environment": "remote"
  }'

Untuk percakapan dan streaming multi-turn, lihat Panduan Memulai. Pola previous_interaction_id dan environment yang sama berlaku untuk agen terkelola.

Agen terkelola juga mendukung eksekusi dan pembatalan latar belakang. Untuk mengetahui detail dan contoh kode, lihat Agen Antigravity: Eksekusi latar belakang.

Mengganti konfigurasi saat pemanggilan

Anda dapat mengganti konfigurasi jaringan system_instruction, tools, dan environment default agen saat membuat interaksi. Hal ini memungkinkan Anda mengubah perilaku, kemampuan, atau kredensial agen untuk operasi tertentu tanpa mengubah definisi agen yang disimpan.

Mengganti petunjuk sistem dan alat

Python

result = client.interactions.create(
    agent="data-analyst",
    input="Analyze Q1 revenue data, but do not create a slide deck. Just output a summary table.",
    system_instruction="You are a data analyst. Focus ONLY on summary tables. Ignore default instructions about slides.",
    tools=[{"type": "code_execution"}], # Override to only use code execution
    environment="remote",
)
print(result.output_text)

JavaScript

const result = await client.interactions.create({
    agent: "data-analyst",
    input: "Analyze Q1 revenue data, but do not create a slide deck. Just output a summary table.",
    system_instruction: "You are a data analyst. Focus ONLY on summary tables. Ignore default instructions about slides.",
    tools: [{ type: "code_execution" }], // Override to only use code execution
    environment: "remote",
}, { timeout: 300000 });

console.log(result.output_text);

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "Content-Type: application/json" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -d '{
      "agent": "data-analyst",
      "input": "Analyze Q1 revenue data, but do not create a slide deck. Just output a summary table.",
      "system_instruction": "You are a data analyst. Focus ONLY on summary tables. Ignore default instructions about slides.",
      "tools": [{"type": "code_execution"}],
      "environment": "remote"
  }'

Mengganti konfigurasi jaringan (memperbarui kredensial)

Jika agen terkelola Anda memiliki kredensial jaringan yang terintegrasi ke dalam base_environment, Anda dapat menggantinya saat pemanggilan untuk memperbarui token yang habis masa berlakunya atau mengganti kunci API. Teruskan objek environment dengan konfigurasi network baru. Aturan jaringan baru sepenuhnya menggantikan aturan sebelumnya untuk interaksi tersebut. Sumber lingkungan dasar (file, repositori) akan dipertahankan.

Python

# Invoke the agent with a fresh token, overriding the base_environment credentials
result = client.interactions.create(
    agent="issue-resolver",
    input="Fix issue #42 and open a PR.",
    environment={
        "type": "remote",
        "network": {
            "allowlist": [
                {
                    "domain": "api.github.com",
                    "transform": {
                        "Authorization": "Bearer ghp_REFRESHED_TOKEN"
                    },
                },
                {"domain": "pypi.org"},
            ]
        },
    },
)

print(result.output_text)

JavaScript

// Invoke the agent with a fresh token, overriding the base_environment credentials
const result = await client.interactions.create({
    agent: "issue-resolver",
    input: "Fix issue #42 and open a PR.",
    environment: {
        type: "remote",
        network: {
            allowlist: [
                {
                    domain: "api.github.com",
                    transform: {
                        "Authorization": "Bearer ghp_REFRESHED_TOKEN"
                    },
                },
                { domain: "pypi.org" },
            ]
        },
    },
}, { timeout: 300000 });

console.log(result.output_text);

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "Content-Type: application/json" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -d '{
      "agent": "issue-resolver",
      "input": "Fix issue #42 and open a PR.",
      "environment": {
          "type": "remote",
          "network": {
              "allowlist": [
                  {
                      "domain": "api.github.com",
                      "transform": {
                          "Authorization": "Bearer ghp_REFRESHED_TOKEN"
                      }
                  },
                  {"domain": "pypi.org"}
              ]
          }
      }
  }'

Mengelola agen

Anda dapat mencantumkan, mendapatkan, dan menghapus agen.

Mencantumkan agen

Python

agents = client.agents.list()
for a in agents.agents:
    print(f"{a.id}: {a.description}")

JavaScript

const agents = await client.agents.list();
if (agents.agents) {
    for (const a of agents.agents) {
        console.log(`${a.id}: ${a.description}`);
    }
}

REST

curl -X GET "https://generativelanguage.googleapis.com/v1beta/agents" \
  -H "x-goog-api-key: $GEMINI_API_KEY"

Mendapatkan agen

Python

agent = client.agents.get(id="data-analyst")
print(agent)

JavaScript

const agent = await client.agents.get("data-analyst");
console.log(agent);

REST

curl -X GET "https://generativelanguage.googleapis.com/v1beta/agents/data-analyst" \
  -H "x-goog-api-key: $GEMINI_API_KEY"

Menghapus agen

Menghapus akan menghapus konfigurasi. Lingkungan dan interaksi yang ada yang dibuat oleh agen tidak akan terpengaruh.

Python

client.agents.delete(id="data-analyst")

JavaScript

await client.agents.delete("data-analyst");

REST

curl -X DELETE "https://generativelanguage.googleapis.com/v1beta/agents/data-analyst" \
  -H "x-goog-api-key: $GEMINI_API_KEY"

Referensi definisi agen

Kolom Jenis Wajib diisi Deskripsi
id string Ya ID agen unik. Digunakan untuk memanggil agen.
description string Tidak Deskripsi agen yang dapat dibaca manusia.
base_agent string Ya ID agen dasar (misalnya, antigravity-preview-05-2026).
system_instruction string Tidak Perintah sistem yang menentukan perilaku dan persona.
tools array Tidak Alat yang dapat digunakan agen. Jika dihilangkan, setelan defaultnya adalah code_execution, google_search, dan url_context. Alat yang didukung mencakup code_execution, google_search, url_context, mcp_server, dan definisi function kustom.
base_environment string atau objek Tidak "remote", environment_id, atau objek konfigurasi dengan sources dan network. Lihat Lingkungan.

Alur kerja iterasi

  1. Buat prototipe dengan agen Antigravity dasar. Teruskan petunjuk sistem dan sumber lingkungan secara inline. Uji petunjuk, keterampilan, dan penyiapan lingkungan secara interaktif.
  2. Stabilkan lingkungan. Instal paket, pasang sumber, pastikan semuanya berfungsi.
  3. Pertahankan sebagai agen terkelola dengan membuat agen baru, baik dari sumber maupun dengan melakukan fork lingkungan.
  4. Perbarui definisi agen. Ubah petunjuk sistem, ganti keterampilan, atau tambahkan sumber. Pemanggilan berikutnya akan mengambil konfigurasi baru.

Batasan

  • Status pratinjau: Agen terkelola dalam status pratinjau. Fitur dan skema dapat berubah.
  • Agen dasar: Hanya antigravity-preview-05-2026 yang didukung sebagai base_agent.
  • Tidak ada pembuatan versi: Pembuatan versi dan rollback agen belum tersedia.
  • Tidak ada subagen bertingkat: Delegasi subagen belum didukung.
  • Anda dapat memiliki maksimal 1.000 agen terkelola.

Langkah berikutnya