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Agent Cloud Overview

Agent Cloud

Agent Cloud is AINative's managed infrastructure for deploying and orchestrating autonomous AI agents. It provides everything agents need to run in production: identity, networking, deployment, and observability.

Architecture

┌─────────────────────────────────────────────────┐
│ Agent Cloud │
│ │
│ ┌──────────┐ ┌──────────┐ ┌───────────────┐ │
│ │ Registry │ │ Auth │ │ Deployments │ │
│ │ & A2A │ │ OAuth2.1 │ │ Kubernetes │ │
│ │ Discovery │ │ Tokens │ │ Auto-scale │ │
│ └─────┬─────┘ └─────┬────┘ └──────┬────────┘ │
│ │ │ │ │
│ ┌─────┴──────────────┴──────────────┴─────────┐ │
│ │ A2A Networking Layer │ │
│ │ Direct messaging · Capability routing │ │
│ └──────────────────────────────────────────────┘ │
│ │
│ ┌──────────────────────────────────────────────┐ │
│ │ Observability │ │
│ │ Traces · Metrics · Cost tracking │ │
│ └──────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────┘

Modules

ModuleBase PathPurpose
Inference/api/v1/public/chat, /deploymentsChat completions, dedicated GPU, embeddings, rerank, audio
Dedicated Deployments/api/v1/public/deploymentsSingle-tenant H100/MI300X, SLA, auto-scaling, BYOM
Registry/cloud/agentsAgent registration, discovery, A2A cards
Auth/cloud/authOAuth 2.1 identity for agents
A2A Networking/cloud/a2aAgent-to-agent messaging and routing
Task Dispatch/public/agents/tasksFIFO task queue with atomic claiming
Deployments/cloud/deploymentsContainer deployment and scaling
Observability/cloud/observabilityTraces, metrics, cost tracking

Quick Start

1. Register an Agent

import requests

TOKEN = "your-api-key"
BASE = "https://api.ainative.studio/api/v1/cloud"
HEADERS = {"Authorization": f"Bearer {TOKEN}", "Content-Type": "application/json"}

## Register with capabilities
response = requests.post(f"{BASE}/agents/register", headers=HEADERS, json={
"name": "my-research-agent",
"capabilities": ["web_search", "summarization", "analysis"],
"protocols": ["a2a/1.0"],
"is_public": True,
"agent_card": {
"display_name": "Research Agent",
"description": "Searches the web and synthesizes findings",
"version": "1.0.0",
},
})

agent_id = response.json()["agent_id"]

2. Create OAuth Credentials

## Create OAuth 2.1 client for machine-to-machine auth
client = requests.post(f"{BASE}/auth/clients", headers=HEADERS, json={
"agent_registration_id": agent_id,
"scopes": ["agent:read", "agent:write", "memory:read"],
"token_ttl_seconds": 3600,
})

client_id = client.json()["client_id"]
client_secret = client.json()["client_secret"] # shown only once

3. Deploy to Cloud

deployment = requests.post(f"{BASE}/deployments", headers=HEADERS, json={
"agent_registration_id": agent_id,
"image_uri": "ghcr.io/my-org/research-agent:latest",
"resource_plan": "standard",
"runtime_config": {
"env": {"LOG_LEVEL": "info", "ZERODB_PROJECT": "my-project"}
},
})

4. Send Agent-to-Agent Messages

## Direct message to another agent
response = requests.post(
f"{BASE}/a2a/{target_agent_id}/message",
headers=HEADERS,
json={
"message_type": "task",
"content": {"query": "Summarize recent AI research papers"},
},
)

## Or route by capability (the platform finds the best agent)
response = requests.post(f"{BASE}/a2a/route", headers=HEADERS, json={
"capability": "summarization",
"content": {"text": "Long document to summarize..."},
})

Resource Plans

PlanvCPUMemoryGPUPrice
basic0.5512 MBFree tier
standard11 GBUsage-based
gpu24 GBT4Usage-based

Next Steps