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Emu Concept – Make AI Follows Your Rules. (Feedback Wanted)

1 points by JariwalaH ↗ HN
The idea is how can we make personal agent apps on the phone just listen to your rules... Like it's weird to have Muse, Grok Bot, Dots, Instinct not ask me about when I want them to work for me or align with my rules, values, and goals.

If I give these agents access to tools/connectors/data they just "assume" what I want or what it should do rather than asking me? And even if I set these for a specific agent then what happens when a newer personal agent comes out? Do I have to reconfigure again my rules for each flavor of personal agents or harness?

This is where our initial idea stems from and we need feedback:

Emu is a self-learning on-device AI guardian that makes AI agents follow your rules. Also the video above kind of just highlights Emu's concept thought process.

17 comments

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verdverm
they aren't going to follow rules reliably, they need to be trained with these recognize ambiguity and ask patterns

unfortunately, the labs have different goals that are not aligned with uses who want this

current training is try anything any everything to get the task done, human free operations... it sells more tokens, they have massive investments and debt to return on

JariwalaH
Well what if we were to put an on-device SLM with our custom RL algorithms, Jev-like capabilities for certain risk thresholds of user’s individual preferences of allowing, denying, and asking user when an agent should and shouldn’t act…(some deterministic enforcement where needed)

Like Muse, Grok bot’s own agents can govern themselves it’ll be kind of bias if the agents knows its own rules and can try to circumvent it or rule hack around if its being trained on RLHF or even if the RLVR optimize for calling tools/data exfiltrating constantly

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ayeeeceee
Hmmm so basically why AI agents get to learn about me but I dont have one set of rules that follows me across all of them

Well I got a chance to look at the website seems interesting enough a mailbox to manage your personal agents with rules? https://trinitite.ai/

JariwalaH
Yeah the idea is to have the human-AI Agent User interaction Experience designed that way really to allow you to manage multiple personal agent apps, AI apps, etc.
aavisangle
Most of these apps work great in labs but fail in real life because everyone has their own different needs and the app should adapt to that. And rules, values and goals change day by day, so agents should adapt to those changes as well.
JariwalaH
Agreed, but every lab has almost trained their own flavor of LLMs/agents being good and “generalized” at certain tasks rather than personalized for the user. (Like Anthropic models are great at design, GPT good at infra code, etc)

Also exactly “rules, values, and goals change day-by-day” is why learning needs to happen but across the apps/agents you’re using to personalize to you… not having to reconfigure every time for new agent or app that’s good at a certain task..

radim_h
How would Emu enforce a denial when the other agent runs in the cloud?

We are building https://stashbase.dev for coding agents, and that’s been the tricky part: a rule needs a place where access can actually be blocked. With local processes you can restrict filesystem and network access; with external services you need control over the credentials or even request path.

Also I think that most "hard" agent permissions and rules should be enforce outside of the model because if the agent is "self-learning" there's a chance it can learn to do something that should not be done. This is a direction we are taking, permission profiles and access that wrap the agents.

JariwalaH
Good questions and pretty cool what you guys have built. There's actually ways to do that or think of it as early days of jailbreaking apps you could theoretically do the same with personal agents...(though i do not want to get into how we do stuff as your'e not the 1st person to ask this, google for startups Global Founder Advocate at TechCrunch disrupt tried asking me this too lol)

Also I would definitely agree with this viewpoint "hard" agent permissions and rules being enforced outside the model, if the model is non-deterministic during inference and hallucinating. Our research paper has shown otherwise for being able to enforce sandboxes lock down agents with 100% precision... check it out - https://trinitite.ai/research/your-sandbox-is-made-of-glass/