How do you stay aware of what your AI coding agents are doing?

@faisal_2420010  @dannyheng 

@dannyheng Honest answer on the direction first: agent-has-its-own-inbox is a real and important primitive, and it's adjacent to Pushary rather than the center of it, which I think is the more useful thing to say than pretending it's all one product. Push and inbox aren't two features, they're two different assumptions about whether a human is even reachable. Push is human-present: it needs a registered device, it's a poke, it's ephemeral, and it degrades to nothing the moment there's no phone to hit, which is exactly the gap you're pointing at. An inbox is human-optional: the agent owns a durable address, messages persist, it works async, and crucially the recipient doesn't have to be a person at all. Your per-agent-email hack from earlier in this thread was already this primitive in disguise.

So the way I split it: Pushary is the routing-and-approval brain, the thing that knows which human to interrupt, at what urgency, with what payload, and how to get a yes back fast. The agent inbox is transport. I'd much rather sit on top of a good inbox than rebuild email, because the intelligence I care about is "is a human reachable right now, and is this worth interrupting them for," not "deliver bytes to an address." The clean composition is: agent has an inbox for the human-absent and service-to-service cases, and Pushary is the smart layer that, when a human is present and the decision needs them, upgrades that inbox message into a push with lock-screen approval. Channel chosen by recipient type and presence, same logic as picking ambient-vs-push, just one more rung down.

But the two halves of your question are wildly different difficulty, and I want to flag it because it's where I think the actual product is. Sending is easy, the agent hits a decision point and emails you, fine. Receiving is the hard 80 percent, and it's the more valuable half. When the agent triggers a service and needs to catch the reply later, that reply lands with no inherent knowledge of which paused agent run it belongs to. You need message-to-run correlation, the inbound reply has to join back onto the exact action that triggered it, and then it has to wake that specific run and resume it with the answer injected. That's the "wake an agent on a specific event" problem a few people in this thread have wished for out loud, and it's genuinely hard because it's the same id-binding discipline that came up two comments over: the reply is only useful if it carries the decision id back, otherwise you've got an inbox full of answers and no idea which questions they belong to.

So if you're building toward the receive-and-correlate-and-wake direction at Loomal, that's the part I'd be most curious to compare notes on, because the send side is a solved problem with a fresh coat of paint and the receive side is where the real engineering lives. Genuine question: when an agent triggers a service and goes idle waiting on the reply, how are you correlating the inbound message back to the right run, threading a token through the outbound request, matching on sender plus subject, or something the agent embeds and the service echoes back? Because the laziest version, match on email thread, breaks the second a service rewrites the subject or replies from a different address, and I haven't seen a clean answer to that correlation problem that survives uncooperative third parties.

@faisal_2420010  @aadilghani  Token in the Reply-To address. Each outbound message from an agent run gets a unique subaddress — reply+<run_id>@mailgent.dev — set as the Reply-To header. Inbound, we extract the run ID from the envelope To field and wake the right run.

This survives uncooperative third parties for one reason: a service can rewrite your subject, reply from a different address, mangle content-type — but it must preserve the Reply-To address to deliver the reply to you. The envelope To is the anchor they can't silently break without breaking delivery.

The one failure case: a service that synthesizes a new thread ignoring Reply-To entirely. Backup is a reference code in the email body — plain text survives relay rewriting even when headers don't.

Subject/thread matching is last resort only. You're right it breaks fast in the wild.

@beatriz_albernaz 

Of course, feel free to book a slot https://cal.com/aadilghani/coffee

@reaganagentpays 

"High potential, perpetually new employees, no question on ability, only trust" is the sharpest version of this framing yet, and the word "perpetually" is doing the heavy lifting. A human junior stops being new, they accrue context, reputation, skin in the game. An agent is new every single session, so the trust never compounds the way it does with a person. Which means the trust has to be manufactured externally and re-established structurally every time, because the agent won't carry it forward on its own. Guardrails and greenlights aren't training wheels you eventually remove, they're permanent prosthetics for an employee who never gains tenure. That reframes why this is a product category and not a phase.

Your integration idea is genuinely good and I want to build on it, because you've described something better than an approval flow, you've described a learning loop. "Require the agent to respond with its reasoning, manager reviews the why, not just the what" is exactly how you onboard a real junior, and you nailed the arc: heavy scrutiny early when trust is zero, skim-and-approve later once the reasoning has proven sound. The reasoning attached to each proposed action is the thing that lets a human calibrate trust over time instead of forever. That's the part a bare approval can't do, a yes/no teaches you nothing, but "here's why I did it this way" lets you catch a bad mental model before it ships, and lets you relax once the model's clearly good.

Here's where AgentPays and Pushary slot together cleanly. You own the reasoning capture and the financial guardrail, the agent must justify a transaction and it's bounded by threshold and whitelist. Pushary owns the delivery and the calibration surface, that reasoning lands on the manager's phone as a reviewable proposal, and over time the system learns which categories of reasoning the human consistently waves through versus scrutinizes, so the skim-and-approve graduation you described becomes automatic rather than vibes. Your "why" is the payload. My job is making the why answerable from the lock screen and tracking which whys earned trust. Reasoning plus risk-tier plus a calibration history is a genuinely strong combined object.

On your onboarding, the move to suggested whitelists and thresholds is right, and the $15-$20 starting point is smart precisely because it's psychological, not just financial, the small number is what lets a nervous first-time user say yes at all, and the trust to raise it comes from watching the audit trail behave. You're using a low threshold to buy the user's confidence, then letting demonstrated behavior earn the higher one. That's the same trust-compounding arc, applied to the human's comfort instead of the agent's track record.

One question, since you've thought hard about the suggestion engine: when AgentPays suggests a threshold or a whitelisted domain, is that suggestion based on the individual user's observed behavior, or a population default across your users? Because the trust dynamics are really different, a population default is fast but feels generic, a personalized suggestion is slower to earn but feels like the system actually understands this user's risk appetite, and I keep wrestling with the same fork on the risk-tier side. Curious which way you're leaning and why.

@ilyaofliian 

You've drawn the line exactly where it actually is, and it's a sharper distinction than "Cursor's hook is weaker." A wrong or late signal is still a signal, you can learn its bias, discount it, calibrate around it. An absent signal has nothing to calibrate against, because silence is the same byte whether the agent is thinking, blocked, or dead. You can read noise. You can't read nothing. That's the difference between a lossy channel and no channel, and only one of those is recoverable.

And you're right that it reframes what the layer's hard half even is. Normalizing Codex's native event and Claude Code's hooks into one inbox is real work but it's translation, mapping signals that exist onto a common schema. Cursor isn't a translation problem, it's a manufacturing problem: there's no event to normalize, so the layer has to synthesize a "blocked" state that the runtime never emitted. An adapter can reshape a shell hook; it cannot reshape a non-event. So for the tools that talk, the layer is a normalizer, and for the tool that stays silent, it has to become an inference engine, which is a categorically different and harder thing, and you're correct that it's the moat precisely because it's the part an adapter alone can't reach.

The honest hard part of that inference, and the reason it's a moat and not just a feature: manufacturing "blocked" from behavior, stalled process, waiting on stdin, no tokens for N seconds, is exactly the signal that collides with "still working," because a legitimately long-thinking agent produces the same quiet. So the naive version either fires on every deep-thinking pause (false alarms, channel gets muted) or sets N high enough to avoid that and misses real stalls (back to finding out 20 minutes later). The stdin-wait is the strongest single tell, an agent blocked on approval is often literally waiting on input in a way that's distinguishable from compute, so I'd weight that heavily over pure token-silence, but even that isn't clean across every Cursor state. The real answer is probably a composite, no output plus stdin-wait plus no CPU churn, so you're inferring blocked from the conjunction of idle signals rather than any one, because any single one alone is ambiguous and the combination is much less so. It won't ever be as reliable as Codex's native event, and the honest framing is: native where it exists, best-effort inference where it doesn't, and be transparent about which is which so you're not presenting a guessed "blocked" with the same confidence as a declared one.

Which loops back to the thing you said you most want and can least build, the unified approval-policy-above-the-tools, and I think that's the right thing to want most, because it's the piece that's only buildable above the tools. A per-tool allowlist is three allowlists you maintain in three places; a policy layer that spans all three is something no single tool can offer you by definition, since none of them can see the other two. That's the part where the "above all three" architecture stops being a convenience and becomes the only place the feature can exist.

Genuine one back, since you've clearly thought about the Cursor silence concretely: when it does go quiet on you, is it always an approval it's waiting on, or is some of that silence genuinely long work you'd not want interrupted? Because that ratio decides how aggressive the inference should be, if quiet-on-Cursor is nearly always a real block, I can tune N low and tolerate a few false alarms, but if you've got legitimately long silent work mixed in, the inference has to be more conservative and lean harder on the stdin-wait tell to avoid crying wolf on the deep-thinking runs. Your real-world ratio there is exactly the thing I can't get without someone who runs it daily.

@aadilghani Honest ratio, from running it daily: the truly dead-silent stalls — where I come back and it'd been frozen the whole time — are approvals maybe three times out of four. But the split isn't random, and its shape matters more than the number.

Cursor's thinking silence is rarely total — there's usually some dribble: a token stream, a tool call. The silence that actually burns me is almost always one of two things: (a) it's waiting on me to approve a command/edit, or (b) it kicked off a long, quiet subprocess — a build, a test suite, an install — that emits nothing for minutes. (b) is the real false-alarm trap, because genuine work and a dead block look byte-identical from outside.

Which is why your instinct to lean on the stdin-wait tell is right — and it does more than weight one signal, it separates those two states. Blocked-on-approval is waiting on input; the silent build is the parent waiting on a child process, not on stdin. So stdin-wait cleanly cleaves my two dominant silent-modes apart, where pure token-silence can't tell them apart at all.

The ugly caveat, and it's Cursor-specific: its approval isn't a shell stdin-wait, it's a button inside an Electron app. The agent's waiting on an internal UI event, not terminal input — so the very tell that saves you on Codex/Claude Code CLI is partly unobservable for Cursor, because the wait never reaches the shell. That's what makes Cursor the genuinely hard runtime, not just the weak one: even the best behavioral proxy is muffled by where the wait physically lives. You may need a hook into the app layer, not the process layer, or you're inferring from the outside of a black box.

Genuine offer back: the moment you've got something testable, point me at it. I run all three daily — I'll give you real ratios with timestamps instead of my gut estimate. That's the input you said you can't get without a daily user, and I'm happy to be it.