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Introducing AgentZ: Zero-Trust AI Agents You Can Deploy Anywhere

Build, run, and govern real agents. Secure by default.

Editorial brief

Slug   /blog/introducing-agentz

Reader   Technical decision-makers at multi-team orgs, compliance-driven enterprises, and teams running many tools. Platform and engineering leads, heads of automation, security-conscious CTOs.

Tone   Confident, category-defining, product-led. Not fear-based. Position AgentZ as an AI platform with security baked in, not as AI for security, and not solely as a security product.

Primary keywords   zero trust AI agents, agentic AI platform, deploy AI agents anywhere, BYO LLM, self-hosted AI agents, AI workflow automation

Pain points to hit   Vendor and model lock-in. No observability or on-prem option in existing agent tools. Secrets exposure risk. Configuration work before a workflow runs. Teams that need agents without low-level configuration.

Alternate titles

  • AgentZ: Build, Run, and Govern Real AI Agents, Secure by Default
  • The Power of Agentic AI, Without Losing Control

The problem with today's AI agents

Model lock-in and the loss of control

Secrets, exfiltration, and the trust gap

Why "agentic" has been hard to adopt in the enterprise

What is AgentZ?

Zero trust AI agents, secure by default

"Build, run and govern real agents": the core value proposition

An AI platform, not only a security tool

The four things that make AgentZ different

Model-agnostic: bring your own LLM

Deploy anywhere: SaaS, on-prem, air-gapped

Secure by default: zero trust, tool-level permissions

Built for enterprise: centralized admin, RBAC, workspace inheritance

How it works

Organizations, workspaces, agents, workflows, sandboxes

Skills and reusable MCP connectors

One control plane for every agent, model and team

A 60-second look at AgentZ in action

A real workflow: created, configured, triggered

Deploy anywhere, on your terms

Deployment models at a glance

Free and Enterprise tiers

Get started

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