Unsure why this feels different from the umpteen other frameworks; maybe it appears to have a human -authored readme, or if it’s just simple enough to fit into my mental model of how agents work.
Whoops, I’ve read too far into the readme. Scratch that first one :)
Anyway, given OP appears to be the author: how are you differentiating from the rest of the (some well funded) guard dogs? The adaptive learning; totp integration; simple tool proxy model; your turn gate state machine’s novelty?
Most of the other frameworks sit on the network peremiter and only inspects the prompts. The biggest competition right now that I see in the same lane Cisco's Defenseclaw which focuses on breadth and has relatively loose security guardrails.
Each piece of tech of the framework is not novel, since I'm not trying to reinvent the wheel here.
My focus is on the depth of security and a set of invariants that the system is built around. It's designed to be overprotective sitting on the execution path fail closed and raise only. The adaptive learning lowers that security based on your needs so eventually it can become a silent protector that doesn't constantly bother you like CC asking you for permission to do a git push.
Also the intro of the readme was written by me, the rest was meant to just be something agents ingest and use to set up Doberman hahaha, didn't think anyone would read the full thing.
Unsure why this feels different from the umpteen other frameworks; maybe it appears to have a human -authored readme, or if it’s just simple enough to fit into my mental model of how agents work.
Whoops, I’ve read too far into the readme. Scratch that first one :)
Anyway, given OP appears to be the author: how are you differentiating from the rest of the (some well funded) guard dogs? The adaptive learning; totp integration; simple tool proxy model; your turn gate state machine’s novelty?
Most of the other frameworks sit on the network peremiter and only inspects the prompts. The biggest competition right now that I see in the same lane Cisco's Defenseclaw which focuses on breadth and has relatively loose security guardrails.
Each piece of tech of the framework is not novel, since I'm not trying to reinvent the wheel here. My focus is on the depth of security and a set of invariants that the system is built around. It's designed to be overprotective sitting on the execution path fail closed and raise only. The adaptive learning lowers that security based on your needs so eventually it can become a silent protector that doesn't constantly bother you like CC asking you for permission to do a git push.
Also the intro of the readme was written by me, the rest was meant to just be something agents ingest and use to set up Doberman hahaha, didn't think anyone would read the full thing.