01
What is AccuKnox AgentZ?
AgentZ is a platform and runtime for building, running, automating and governing AI agents. AccuKnox describes a system where teams define agents and reusable skills, connect tools, run workflows from chat, APIs or a CLI, and trigger repeated work on a schedule or through webhooks.
The product is aimed at the operational layer around an agent: where it runs, which models and tools it can use, what network destinations it can reach, how credentials are supplied and how a completed run can be inspected.
02
Is AgentZ open source?
Yes. AccuKnox publishes the AgentZ repository on GitHub, and the repository declares the Apache License 2.0. That is more precise than relying only on the product page's open-source label.
The repository contains the application source together with deployment material, including Helm and Kustomize directories. Anyone deploying it should still review the repository, dependencies and current release state before treating a self-managed installation as production-ready.
03
Hosted, on-premises and air-gapped availability
AccuKnox says the hosted service is available at agentzharness.ai. The company also advertises on-premises and air-gapped deployments for teams with infrastructure, compliance or data-residency requirements.
The public repository supports self-managed evaluation, but a repository and deployment manifests are not the same as a fully documented production support contract. Teams should confirm the current installation path, required Kubernetes infrastructure and support terms with AccuKnox before a production rollout.
04
AgentZ pricing and the free plan
AccuKnox's documentation says AgentZ is free to start, and the launch announcement describes a hosted free plan. As of the August 27 factual review, no detailed public AgentZ price table, paid-tier pricing or verified free-plan quotas were found.
05
Models, connectors and MCP support
AgentZ is model-agnostic. Its documentation lists OpenAI, Anthropic, Google Gemini, AWS, Microsoft Azure and open-weight models, while the repository README adds provider paths such as Amazon Bedrock, Vertex AI, Azure AI Foundry and custom OpenAI- or Anthropic-compatible endpoints.
AccuKnox lists connectors for Slack, Gmail, Microsoft 365, Google Workspace, Jira, Confluence, Notion, GitHub, GitLab and Bitbucket. The repository also describes an MCP server catalog and the ability to add a custom MCP server.
06
How the AgentZ sandbox and permissions work
According to AccuKnox, each agent runs in a sandbox with a default-deny network posture. The open repository explains that an agent is a Kubernetes pod and that Cilium network policies block outbound traffic until an explicit rule permits it.
AccuKnox also says credentials are scoped and injected at runtime instead of being exposed directly to the agent. Its documentation describes domain, port and protocol controls, per-action permissions and role-based access, while the repository describes host-scoped secret injection.
07
Workflows, traces and governance
AgentZ organizes work around organizations, workspaces, agents, workflows and sandboxes. Skills provide reusable capabilities; workflows chain steps; schedules and webhooks trigger runs; teams and roles define ownership and access.
The product also records workflow graphs, execution traces, agent activity and tool interactions. That matters when an agent can change a business system: the final answer alone does not explain which tools were called, which permissions applied or where a failure occurred.
08
Who is AgentZ for?
AgentZ is most relevant to teams that want agents to perform repeatable work across business or engineering systems and need more control than a standalone chat interface provides. AccuKnox's examples span security, sales intelligence, research, engineering automation, HR, finance and operations.
For a small personal automation, Kubernetes-based governance may be more infrastructure than necessary. For organizations allowing agents to use credentials, call tools or mutate external systems, isolation, permissions and traceability become more important.
09
AgentZ compared with agent frameworks
A direct benchmark-style comparison with LangGraph or CrewAI would be premature. Those projects are commonly used to define and orchestrate agent behavior; AgentZ is positioning itself as a broader runtime and governance layer around execution, credentials, networking, deployment and auditability.
That difference in scope does not make one approach universally better. A useful future comparison would need a shared workflow, equivalent deployment assumptions, connector depth, operational overhead and reproducible security tests.
10
What to watch next
- A public AgentZ pricing table and free-plan quotas
- Stable self-hosting installation and upgrade documentation
- Connector-specific permissions and supported actions
- Release maturity, adoption and independent security review
- Reproducible comparisons with orchestration frameworks
Sources
Primary and supporting sources
Facts were rechecked against the linked sources immediately before publication. Pricing, product availability and rollout status can change.