How is AxLoop different from a traditional endpoint inventory?

A conventional inventory models device → application → version. AxLoop models the AI relationships forming on the device: AI clients, agents, IDEs, runtimes, MCP clients, and MCP server declarations, plus how they change over time.

Is AxLoop a replacement for an endpoint security platform like Glow?

No. Glow provides broad endpoint software discovery, risk context, policy, remediation, and prevention. AxLoop is narrower and more AI-specific: an AI-native discovery layer that can complement a broader endpoint control platform.

Why does MCP change endpoint inventory?

MCP introduces relationships that may not appear in a traditional software catalog: which AI clients support MCP, which server names appear across the fleet, which transport is configured, which configuration source introduced a declaration, and what changed.

Does AxLoop collect employee content?

No. AxLoop's endpoint architecture produces inventory metadata without collecting prompts, responses, source code, credentials, raw environment variables, or complete command lines.

All comparisons

Comparison · Endpoint security

AxLoop vs Glow: AI-Native Discovery and Endpoint Control Compared

Compare AxLoop's AI-native endpoint discovery with Glow's broader endpoint software inventory, security policy, and remediation approach.

2026-09-25

Endpoint security platforms have long answered questions about installed software, risk, policy, and remediation. AI changes the shape of that inventory. An employee device may contain an AI desktop application, several coding assistants, an IDE extension, an agent runtime, a local model server, one or more MCP clients, and multiple MCP server declarations. That requires a more relationship-aware view of the endpoint.

Glow and AxLoop approach this problem from adjacent directions.

Where Glow starts

Glow positions itself as an endpoint security platform. Its public product story includes endpoint software discovery, asset intelligence, continuous inventory, software risk context, policy, remediation, prevention, and AI footprint visibility. That gives Glow a broad endpoint-control orientation.

Where AxLoop starts

AxLoop is narrower and more AI-specific. Its current endpoint model focuses on identifying supported AI applications, coding agents, AI CLIs, local model runtimes, AI-related processes, IDE extensions, MCP clients, MCP server declarations, and configuration changes. The product is not simply trying to reproduce a traditional software inventory. It is trying to describe the AI relationships forming on the device.

Traditional endpoint inventory

A conventional software inventory can often be modeled as:

DEVICE
  → APPLICATION
  → VERSION

That remains useful. But AI infrastructure increasingly looks like:

DEVICE
  → AI CLIENT
  → AGENT
  → IDE
  → RUNTIME
  → MCP CLIENT
  → MCP SERVER DECLARATION

That is a different asset model.

Why MCP changes endpoint inventory

MCP introduces relationships that may not appear in a traditional software catalog. A security team may need to know: Which AI clients support MCP? Which clients have server declarations? Which server names appear across the fleet? Which transport is configured? Which configuration source introduced the declaration? Which endpoint owns that configuration? What changed? AxLoop's current macOS implementation is designed to treat these as first-class discovery signals.

Broad endpoint control vs AI-native inventory

The easiest way to understand the distinction: Glow starts broadly with the endpoint and software estate, then adds security and control. AxLoop starts deeply with the AI-specific estate on the endpoint. The approaches can overlap without being identical.

Where they overlap

Both categories can help answer: What software exists? Where does it exist? Is it approved? Did something new appear? Which endpoint owns it? Is there unmanaged AI adoption?

Where AxLoop goes deeper conceptually

AxLoop's data model is designed around AI-specific objects and relationships. Instead of only asking “Is Cursor installed?” the system can evolve toward questions such as: Is Cursor present? Which version? Which AI-related process is running? Which supported MCP declarations exist? Which client configuration introduced them? What changed from the last observation? This is the difference between generic software inventory and AI-native inventory.

Privacy and telemetry

AxLoop's endpoint architecture is designed to produce inventory metadata without requiring collection of prompts, responses, source code, credentials, raw environment variables, or complete command lines. This makes the product useful as an AI asset-discovery layer even in environments where security teams do not want another endpoint product collecting employee content.

Do organizations need both?

Potentially. A broad endpoint security platform can provide software control, remediation, endpoint policy, and broader device security. An AI-native discovery layer can add deeper AI classification, MCP relationships, AI-specific inventory context, and AI configuration visibility. The two categories solve adjacent problems.

AxLoop perspective

AI inventory is becoming its own security discipline. The object being managed is no longer only an application. It may be an application, containing an agent, configured to use an MCP server, which exposes tools, from an endpoint owned by a particular user or team. AxLoop starts by making that estate visible.