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Hurozo Overview

Enterprise Agent Platform

Cut approvals from weeks to days

Coordinate specialists, approvals, and systems without handoffs falling through the cracks. Hurozo helps teams orchestrate AI agents like your best cross-functional crew—consistent, auditable, and ready for any scenario.

Access is rolling out via a waitlist. Share your use case and we’ll reach out the moment your workspace is approved.

Book a Pilot Explore Solutions
  • Visual workflow canvas
  • Reusable, governed automations
  • Hybrid human + AI approvals
  • Always-on scheduling & retries

Orchestrating With Hurozo

Flexible by design

4 ways to design and interact with agents on Hurozo

Design agents visually by connecting nodes on a canvas. Drag, drop, and orchestrate complex work with clear inputs, outputs, and guardrails.
Connect Hurozo to AI platforms through the Model Context Protocol. Our MCP server lets copilots call your Hurozo agents and data securely.
Run an agent from Python using the Hurozo SDK. Create an agent, feed inputs, and execute it directly in your environment.
Implement and self-host custom agents in Python. Define inputs and outputs and let Hurozo run them as remote executors.
Visual node-based agent design in Hurozo 
from openai import OpenAI

client = OpenAI()

resp = client.responses.create(
    model="gpt-5",
    tools=[
        {
            "type": "mcp",
            "server_label": "hurozo-mcp-server",
            "server_description": "Hurozo agents",
            "authorization": "<your api token>",
            "server_url": "https://mcp.hurozo.com/sse",
            "require_approval": "never",
        },
    ],
    input="What are my upcoming calendar appointments?",
)
from hurozo import Agent

my_agent = Agent("AgentName")
my_agent.input({"my_input_name": "Hurozo is awesome!"})
print(my_agent.run())
from hurozo import Node

def my_amazing_node(name):
    outputs = {
        'greeting': f'Gwuaaak {name}',
        'shout': f'GWUAAAAK {name.upper()}'
    }
    return outputs

def main():
    Node(my_amazing_node, {
        'inputs': ['name'],
        'outputs': ['greeting', 'shout']
    })

if __name__ == '__main__':
    main()

Proof & Use Cases

How we solve the limitations

Agents that learn, share context, and keep judgement in the loop

Adapts to Change

Autonomous reasoning adjusts capacity, messaging, and compliance steps when conditions shift.

Keeps Context Flowing

Shared memory between agents eliminates repeated briefings and preserves insights across teams.

Scales Human Judgement

Approval routing keeps decision-makers involved only when the stakes demand it—and captures their rationale.

Improves Every Run

Systematic orchestration captures outcomes and continuously tunes workflows for the next iteration.

Documentation

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