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Automox MCP Server 2.2 Brings Visual Review, Agentic Patch by Severity Policy Creation, and Live Capability Discovery to Endpoint Operations

New release makes AI-assisted endpoint management more visual, trustworthy, and actionable for IT teams

Automox released Automox MCP Server 2.2, adding interactive review surfaces, first-class Patch by Severity policy creation, and live capability discovery to its governed agentic interface for endpoint operations.

The release advances Automox MCP beyond natural-language access alone, giving IT teams new ways to review, approve, and act on endpoint operations in context.

"AI agents are only as useful as the platform coverage and governance behind them," said Jason Kikta, CTO at Automox. "MCP 2.2 gives IT teams a more visual, more trustworthy way to bring AI into endpoint operations, without giving up the control that enterprise IT requires."

For IT teams already using Automox MCP, the impact is concrete.

"An MCP server allows us to query live endpoint data using natural language, combine information in meaningful ways, and generate custom visualizations that go far beyond predefined dashboards. It provides an accurate picture of our environment and gives us the ability to answer the questions that matter most, including the ones we haven't yet thought to ask," said Clayton Williams, IT Project Manager, City of Beaumont, TX.

What's new in MCP Server 2.2

Interactive in-host review surfaces. Supported MCP Apps-capable hosts can now render compliance posture, patch approval queues, policy blast-radius previews, remediation-apply reviews, and RBAC access-certification reviews directly inside the assistant experience. IT teams can review posture and pending decisions visually instead of parsing text output.

Patch by Severity policy creation. Users can create Patch by Severity policies agentically, selecting any combination of Automox severity levels. Teams can move from natural-language intent to governed patch policy creation faster, without building the policy manually in the console first.

Live capability discovery. The AI agent can now see live tool availability based on read-only mode, module filtering, credentials, and opt-in safety flags. Safety-gated tools surface the exact setting required to enable them, so IT teams know what's available and how to unlock what isn't.

Structured fallback for unsupported hosts. Interactive surfaces render visually in supported hosts, while unsupported hosts receive the same information as clean, structured data. Customers do not lose functionality based on their MCP client.

Automox MCP Server covers the published Automox Console and Webhooks APIs, excluding only secret-exposing operations by design. Customers get complete AI power tools for Automox rather than a narrow connector or a curated set of workflows.

Automox MCP Server is available now through PyPI, the MCP Registry, and as a one-click Claude Desktop extension. 

For technical documentation, visit docs.automox.com

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Automox MCP Server 2.2 Brings Visual Review, Agentic Patch by Severity Policy Creation, and Live Capability Discovery to Endpoint Operations

New release makes AI-assisted endpoint management more visual, trustworthy, and actionable for IT teams

Automox released Automox MCP Server 2.2, adding interactive review surfaces, first-class Patch by Severity policy creation, and live capability discovery to its governed agentic interface for endpoint operations.

The release advances Automox MCP beyond natural-language access alone, giving IT teams new ways to review, approve, and act on endpoint operations in context.

"AI agents are only as useful as the platform coverage and governance behind them," said Jason Kikta, CTO at Automox. "MCP 2.2 gives IT teams a more visual, more trustworthy way to bring AI into endpoint operations, without giving up the control that enterprise IT requires."

For IT teams already using Automox MCP, the impact is concrete.

"An MCP server allows us to query live endpoint data using natural language, combine information in meaningful ways, and generate custom visualizations that go far beyond predefined dashboards. It provides an accurate picture of our environment and gives us the ability to answer the questions that matter most, including the ones we haven't yet thought to ask," said Clayton Williams, IT Project Manager, City of Beaumont, TX.

What's new in MCP Server 2.2

Interactive in-host review surfaces. Supported MCP Apps-capable hosts can now render compliance posture, patch approval queues, policy blast-radius previews, remediation-apply reviews, and RBAC access-certification reviews directly inside the assistant experience. IT teams can review posture and pending decisions visually instead of parsing text output.

Patch by Severity policy creation. Users can create Patch by Severity policies agentically, selecting any combination of Automox severity levels. Teams can move from natural-language intent to governed patch policy creation faster, without building the policy manually in the console first.

Live capability discovery. The AI agent can now see live tool availability based on read-only mode, module filtering, credentials, and opt-in safety flags. Safety-gated tools surface the exact setting required to enable them, so IT teams know what's available and how to unlock what isn't.

Structured fallback for unsupported hosts. Interactive surfaces render visually in supported hosts, while unsupported hosts receive the same information as clean, structured data. Customers do not lose functionality based on their MCP client.

Automox MCP Server covers the published Automox Console and Webhooks APIs, excluding only secret-exposing operations by design. Customers get complete AI power tools for Automox rather than a narrow connector or a curated set of workflows.

Automox MCP Server is available now through PyPI, the MCP Registry, and as a one-click Claude Desktop extension. 

For technical documentation, visit docs.automox.com

The Latest

For fifteen years, observability lived downstream of everything else. Code shipped, something broke, an engineer went to the dashboards. The job was forensic. The pillars we built, such as logs, metrics, and traces, were designed for that role: tell a human what just happened, fast enough that they can make it stop. That role has quietly ended ...

Hybrid IT has become the standard operating model for enterprises — but that companies are still looking for the right hybrid IT mix, according to the 2026 State of the Data Center Report from CoreSite. After years of cloud migration and hybrid adoption, organizations are shifting their focus from deciding whether to use cloud, colocation or on-premises infrastructure to determining which workloads belong in each environment ...

Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale. But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within ...

Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...

I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...

The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools ...

For years, secure connectivity has relied on a familiar pattern: route traffic back to centralized gateways, inspect it, and then allow access. This model worked when applications lived in a handful of data centers and users were largely confined to offices. That model is now under strain. Applications are distributed across clouds, users connect from everywhere, and real-time workloads demand performance that centralized inspection points struggle to deliver. As traffic volumes grow and latency expectations shrink, routing everything through a small number of control points has become both a performance bottleneck and a resilience risk. The future of secure connectivity requires a different approach ...

The AI experimentation phase is over, and the private cloud is where enterprise AI workloads are being deployed for security and scale, according to Private Cloud Outlook 2026, a new report from Broadcom ... 2026 marks an acceleration into a full AI tipping point. The shift is being shaped by three forces — costs, complexity, and control — that public cloud environments are increasingly failing to address for production AI at scale. Key findings from the report include ...

44% of organizations have reported an outage in the past year tied to suppressed or ignored alerts, and 78% had at least one incident where no alert was fired at all ... Engineers learned about failures from customers. That gap between what our tools report and what our customers experience is the problem DevOps teams have been quietly solving with GenAI tooling, even as most enterprises continue to run their NOCs on manual alert triage ...

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...