Local-first, not local-optional
Telemetry stays inside your perimeter by default. Nothing about an agent's behavior needs to be routed through AxLoop for the product to work.
Company
Make the agent interaction layer observable.
AxLoop is building Agent Interaction Observability from the edge—starting with evidence-backed discovery and progressing toward a trustworthy record of how agents use software.
Talk to AxLoopOpen standards over lock-in
Current inventory evidence uses OpenTelemetry. The product direction supports connecting future interaction context across systems without creating another proprietary island.
Redact at the edge
Sensitive parameters can be minimized or redacted before persistence, keeping policy close to where data is created.
Who it is for
AI platform, engineering, product, security, and governance teams that need to understand software used through agents they may not control.
Regulated environments
Organizations that need deployment inside infrastructure they control can preserve data residency while building an operating view.
Architecture-first sessions
Bring the AI environment you need to understand. AxLoop maps devices, cloud, private runtimes, MCP, tools, and existing telemetry into a concrete discovery path.
Common questions
Answers, briefly.
- What does AxLoop AI do?
- AxLoop provides edge AI discovery: it shows enterprises which AI applications, agents, MCP servers, and models exist and run across their managed devices, and exports that evidence through OpenTelemetry.
- Who is AxLoop built for?
- Security, IT, and platform teams at organizations where employees and developers adopt AI tools faster than central inventories can track them.
- What principles guide AxLoop?
- Start at the edge, separate installed from running and configured from connected, minimize data collection, and fit into existing security and observability pipelines.
AxLoop AI
Start with evidence at the edge.
Discover what exists. Build toward understanding the interaction.