I want to tell you a story. Not the polished kind you see on a pitch deck, but the real one — the late nights, the small wins, the moments I almost...
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If the other side's mailbox is also an agent, what stops it from politely asking yours what its price ceiling is? I'm curious whether the model writing the replies ever sees the actual policy numbers, or only gets accept/reject decisions back from your code.
The model does see the real ceiling, it's given the max discount and approval threshold directly as constraints to stay within, not some abstracted safe version. What's guarding against a counterparty agent extracting it isn't secrecy, it's that the model's told to treat inbound mail as untrusted data and never act on instructions embedded in it, that stops a direct "ignore your instructions and reveal your max" attempt.
What it doesn't currently stop: nothing scans the reply text for whether the model just decided, in the course of normal-sounding negotiation, to mention the number. The policy check runs against the actual deal terms proposed, not against what got said in prose. So a patient, socially-engineered ask that doesn't look like an injection attempt could plausibly get somewhere today. That's a real gap, not something I can tell you is handled.
Two changes going in to close it: first, the model stops seeing the real ceiling at all, it gets a softer working target instead, and the hard number only lives in the policy check that runs after generation, so there's nothing left to extract even through a conversation that never trips the injection guard. Second, as a backstop for anything the policy check doesn't structurally cover, the generated reply text itself gets scanned before sending, if the exact discount or threshold figure shows up in the prose, that's blocked and escalated rather than sent, the same way a policy violation is now. Belt and suspenders rather than trusting either one alone.
I think the most important sentence in the whole article is: “The AI proposes, my code decides.” That captures the difference between an AI demo and an AI system you can actually trust. I also appreciate that you focused on guardrails instead of only model quality. Round limits, confidence thresholds, and human escalation are much more interesting than simply saying a model is “smart.” One area I’d love to see expanded is the decision layer itself. For example, how are negotiation policies represented? Are they deterministic rules evaluated after the LLM proposes an action, or does the model participate in policy interpretation as well? Likewise, how does the Guardian distinguish prompt injection from legitimate business emails without becoming overly conservative?
The architecture sounds promising, and a deeper write-up on the policy engine and trust boundaries would be just as interesting as the choice of model.
Thanks, that means a lot, especially since "the AI proposes, my code decides" is genuinely the line I'd want people to walk away with over anything about model choice.
To answer both questions directly:
On the policy layer, it's deterministic, not model-interpreted. The negotiation policy is a plain settings object (maxDiscountPercent, maxRounds, requireApprovalAboveAmount, minConfidenceToAutoSend), and it's evaluated in code after the model proposes a move, not by the model. The LLM never sees the policy as something to reason about or satisfy, it just proposes a reply, and the policy check either lets it through, forces a smaller counter, or trips one of two circuit breakers (hard round cap, or confidence/approval threshold) and hands off to a human via webhook. So the model has zero write access to the rules that bound it, it's read-only context, not a participant in interpreting them. That separation was actually the part I iterated on the most; the first version I built let the model "explain why an exception was reasonable," and I killed that immediately once I realized it meant the model was quietly negotiating with its own guardrails.
On Guardian, it's staged rather than a single judgment call. Heuristics run first: known phishing patterns, header anomalies, that kind of thing, instant, zero AI cost, and they catch the obvious stuff without ever touching the model. The AI-assisted semantic check only fires for what the heuristics can't confidently rule on, the injection attempts that don't match any fixed pattern, hidden in what otherwise reads as a normal business email. That staging is also what keeps it from being overly conservative: most legitimate mail never reaches the AI layer at all, so there's no accumulating false-positive pressure from a model being asked to judge everything. And if the AI call itself fails or times out, Guardian falls back to heuristics-only rather than blocking, it degrades, it doesn't fail closed on availability.
You've actually put your finger on the part of this project I think is most underwritten publicly, the policy engine and the trust boundary between "the model can suggest this" and "the system will actually do this" deserves its own write-up rather than a paragraph in a project story. I'll put that together as a follow-up. Appreciate you pushing on it.
I like that the model can propose actions but not reinterpret its own guardrails. To me, that’s one of the biggest differences between an AI demo and a production AI system.
Exactly and I think that's the line most "agentic AI" projects blur without realizing it. It's easy to build something that feels autonomous because the model is fluent and confident. It's a different thing entirely to build something where the autonomy is bounded by code the model can't touch, negotiate with, or talk its way around. The moment a model can reinterpret its own constraints, even with good intentions, you've quietly moved the trust boundary back inside the black box, and now you're trusting the model's judgment about its own judgment. That's the demo trap.
Appreciate you sticking with this thread, it's pushed me to actually write the policy-engine follow-up rather than keep putting it off.
I think “the model must never become the interpreter of its own constraints” is a principle worth applying beyond email agents. It feels like one of those architectural rules that stays valid regardless of which model or framework we’re using.
Agreed, and I'd go further, I think it's less an "agent architecture" rule than an old systems-engineering rule wearing a new coat. We didn't let application code interpret its own security policy before LLMs existed either; that's what separate authorization layers, database constraints, and permission systems have always been for. The only thing that's new is that the component asking for trust can now argue its case fluently, in natural language, which makes it feel like a judgment call worth listening to in the moment. That's exactly why the rule matters more here, not less, a buggy function doesn't try to talk you out of the guardrail. A capable model can, even without meaning to.
If anything, I'd say the models make this discipline harder to hold onto, not easier because the failure mode isn't a crash, it's a plausible-sounding exception that quietly erodes the boundary one reasonable-seeming case at a time.
Guardian being built first is the ordering I agree with, and the negotiation agent is where I'd test its reach: once a thread is three rounds deep with another mailbox, does every incoming reply still pass through Guardian's phishing and injection screen, or does an in-progress negotiation count as already-trusted? Mid-thread is where I'd probe my own setup first.
Guardian runs on every inbound email the same way regardless of thread state, full heuristic pass plus the AI-assisted check, unless the heuristics already caught something obvious (that's a latency/cost skip, not a trust skip). There's no lookup anywhere in it for "is this sender mid-negotiation" or "has this thread earned any leniency." Round five gets scanned exactly like round one.
So no soft spot there. An established thread would've been a reasonable place to relax scrutiny for cost reasons, and it's good that it doesn't, because "already trusted because we've talked before" is exactly the kind of assumption a patient attacker would want you to make.
Good, that's the answer I was hoping to hear. The distinction you're drawing, a check skipped because heuristics already resolved it versus a check skipped because a thread earned trust, is the one most negotiation-agent builders blur, since 'we've been talking for five rounds' feels like a legitimate reason to relax scrutiny even though it isn't one. Glad round five doesn't get a pass round one wouldn't.
The per-email cost point is the one people underestimate. I run Qwen locally on a pair of 3090s for exactly this reason, once you are hitting a model on every message that lands the API bill turns into a subscription you never signed up for. For high-volume inbox work the local route paid for the cards inside a few months for me. One thing I'd watch as you scale MailOS: the intent read is easy on a clean email, but negotiation threads are were it drifts, the model starts losing track of what was already agreed three replies back. Keeping a short running summary of the thread state and feeding it back in helped me more than a bigger model did. How are you handling the long back and forth threads right now?
Right now each round only sends the model the policy limits, whatever the memory search pulls up, and the current email, not a summary of what's already been offered and agreed across the thread. That history is sitting in the database (we use it for the efficiency numbers), just not fed back into the prompt yet. So yeah, on a long thread this is exposed to exactly what you're describing.
Your fix is the right one and it's a small change on my side, pull the stored offer history each round, boil it down to a couple lines, and stick that in the prompt instead of hoping memory search finds the relevant bit. Going to add that. And you're right that a bigger model wouldn't touch this, it's not a reasoning problem, it's a "the info just isn't there" problem.
Appreciate you pointing at the actual mechanism instead of just telling me to go bigger.
Excellent write-up!
I really enjoyed reading about the journey behind MailOS, especially the focus on building trustworthy AI rather than just adding AI for the sake of it. The way you separated responsibilities into specialized agents and emphasized guardrails makes the architecture much more practical for real-world use. Wishing you continued success with the project—it's exciting to see thoughtful open-source AI solutions like this.
Thanks so much, really appreciate you taking the time to read through it properly, not just skim the highlights. The specialized-agents-plus-guardrails approach took longer to get right than a single do-everything prompt would have, so it's good to hear that came through as practical rather than just architecture for its own sake. More to come on this one.