Shogun AFM is an Agent Fleet Management framework for running, monitoring, governing, and controlling self-improving AI agents across tools, workflows, memory, models, and business environments.
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I built Shogun AFM because I got tired of spending hours in PowerShell just to get AI agent frameworks running. Projects like OpenClaw and Hermes showed me both the potential and the challenge: installation complexity, fragmented tooling, and very little focus on governance once agents are deployed.
The more I explored agent frameworks, the more I realized there was another problem. Organizations don't just need to build agents—they need to monitor them, govern them, discover them across networks, enforce security policies, and shut them down when necessary.
Shogun AFM started as an attempt to make AI agents easier to deploy. It evolved into Agent Fleet Management: a platform focused on operating, governing, and controlling AI agents at scale.
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