Axonius launches AI Workforce virtual workers as AI Companion and MCP Server go live

Axonius launches AI Workforce virtual workers as AI Companion and MCP Server go live

Asset intelligence company Axonius Inc. today added Axonius AI Workforce to its Asset Cloud platform to let security teams hand ongoing remediation work to autonomous, artificial intelligence-powered virtual workers.

AI Workforce is meant to take over follow-up work that security teams rarely have time for. A lot of that work exists because asset data ends up split across dozens of tools, and the scheduled jobs most teams rely on only catch conditions somebody thought to define ahead of time. Anything outside those rules is left for a person to sort out, Axonius says, down to working out why a single endpoint agent stopped reporting.

Two tools Axonius announced in July also moved to general availability today. The Axonius AI Companion takes plain-language questions about a customer’s environment and can act on the answer in the same conversation, opening a ticket or tagging devices when asked. Outside AI tools and agents get at the same asset data through the Axonius MCP Server, which runs over the Model Context Protocol.

The virtual workers at the center of the launch come in more than 50 types, covering security outcomes that include exposure triage, shadow AI and readiness for post-quantum cryptography. Every worker tracks a metric tied to its outcome, which is meant to save teams from building custom reports to see if anything is improving. When an asset drifts from the state it should be in, the worker responsible for it carries out the fix, and the results feed a Daily Digest that Axonius ranks by urgency.

A mode setting controls how far a worker goes on its own, and Axonius recommends a starting mode for each job based on its own best practices. In Off mode, the worker waits for approval on everything. Teams that want to watch first can use Shadow mode to preview what the worker would do, then switch to Live and let it act automatically within set guardrails. Outbound messages and changes to connected systems count as sensitive and are held for approval before they execute.

Each customer also runs a private large language model instance inside its own dedicated environment, and an audit trail marks every action as either agent-driven or human-driven.

Moshe Ben Simon, chief product officer at Axonius, said every vendor is shipping AI agents now and “the question nobody is answering is what those AI agents are acting on.” An agent that acts on one vendor’s incomplete view of an asset is going to be wrong, he said.

Axonius spent years building a platform that draws on more than 1,400 bidirectional integrations, and Ben Simon described AI Workforce as what can safely be built on top of it. The company made the same data-quality argument in April, when it launched Axonius Verified Assets on the premise that AI agents act on their inputs without questioning them.

AI Workforce, the AI Companion and the MCP Server all run on the same asset model that already powers the Asset Cloud. Axonius builds it by reconciling and deduplicating data across more than 80 asset and exposure entity types. If the software can’t verify something, it is designed to say so.

Theresa Lanowitz, principal analyst for cybersecurity at Omdia, said what stands out is how directly the launch ties autonomous remediation to a verified view of the environment. She expects that pairing, plus human oversight where it matters, to separate AI that security teams “can actually trust” from tools that merely add noise.

AI Workforce is generally available to Axonius customers now. Security engineering teams can see how many AI credits their users and the virtual workers spend, and what that costs.

Photo: Axonius