AI Agent Platform

Build, Deploy, and Orchestrate AI Agents for Your Infrastructure

Run local LLMs, create custom agents with no code, orchestrate multi-agent teams, and automate complex workflows — all self-hosted on your own servers.

6Built-in Agents
4LLM Providers
40+API Endpoints
ZeroCloud Lock-in
Agent Builder

Create Custom AI Agents — No Code Required

Design agents that understand your infrastructure. Attach tools like log readers, container inspectors, and metric queries. Test in a sandbox before deploying to production.

  • Drag-and-drop tool attachment
  • 8+ built-in tools (logs, containers, metrics, alerts)
  • Sandbox testing environment
  • One-click deployment (draft → production)
  • Custom system prompts and personas
agent-builder
Troubleshoot Agent
GPT-4o
Diagnose and resolve problems with installed applications. Analyze logs, inspect containers, and suggest fixes.
read_app_logsinspect_containerquery_metricsget_alerts
Test in SandboxDeploy →
agent-team
Team: Infrastructure Response
Leader
Debug Agent
Perf Agent
Security Agent
Diagnosing memory leak in auth-svc...AIAgents.agentTeams.mockup.progress%
Multi-Agent TeamsPRO+

Collaborative AI That Works Together

Create teams of specialized agents that delegate tasks, share context, and solve complex problems collaboratively. A leader agent coordinates the workflow while team members execute in parallel.

  • Leader-mediated task delegation
  • Parallel execution across members
  • Role-based specialization
  • Bounded iterations prevent runaway loops

AI Hub

Your Models, Your Servers, Your Data

Run Ollama or LocalAI on your own hardware. Pull models from the library, allocate GPU resources, and fall back to cloud providers when needed — all with per-provider budget tracking.

Ollama

running

OpenAI

connected

Anthropic

connected

LocalAI

available

Google Gemini

connected

Custom

available
quazzar-ai
> Pulling llama3.2:latest...
78% (4.2 GB)
GPU: NVIDIA RTX 4090 (24 GB VRAM)
Models loaded: 3 | Budget: $42 / $100
Routing: local-first → cloud-fallback
Workflows

Automate Complex Tasks with AI-Powered Pipelines

Build DAG-based workflows that chain agents, conditions, HTTP calls, and transforms. Schedule them on cron or trigger manually. Monitor execution in real-time.

  • 6 step types: agent, team, condition, transform, HTTP, delay
  • DAG execution with conditional branching
  • Cron scheduling for automated runs
  • Real-time WebSocket monitoring
  • Remote cluster node targeting
workflow — Incident Response
Alert Trigger
Diagnose
Critical?
critical
Team Response
Notify Slack
warning
Generate Report
Log Results
Complete Active Pending

More capabilities

Built-in intelligence at every layer

Retrieval-Augmented Generation

RAG System

Vector embeddings, document chunking, and similarity search give your agents context-aware responses grounded in your actual infrastructure data.

Model Context Protocol

MCP Protocol

Connect external tools and services to your agents via the open MCP standard. Expose and consume tools across your infrastructure.

Custom Model Training

Fine-Tuning

Train and evaluate models on your own data. Built-in evaluation pipeline measures quality so you ship with confidence.

Budget-Aware AI

Cost Tracking

Per-provider spending limits, token analytics, and automatic fallback to local models when budget thresholds are exceeded.

Ready to Build Your AI-Powered Infrastructure?

Start with built-in agents for free, or unlock the full platform with custom agents, teams, and workflows.