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

@senk0 

That's a legit pragmatic move, and the instinct underneath it is the right one: matching how much you trust the review to how much the change actually matters. For low-stakes updates, a quick LLM summary is plenty, and reading the full diff yourself would be over-verifying something that didn't need it. You're triaging your own attention, spending it where the risk is and saving it where there isn't. Most people either review everything or nothing, and you've found the sane middle.

The one place I'd flag caution, and it's the exact spot this trick is weakest: an LLM summarizing the agent's own answer is asking one model to vouch for another, and it inherits the original's blind spots. If the agent quietly skipped a check or made a wrong assumption, the summarizer often won't catch it, because it's summarizing the narrative, not auditing the actual diff. It's great at "what does this say it did" and unreliable at "did it actually do that, and what did it leave out." So it works precisely because you're applying it to non-serious updates, the moment it touches something risky, summarizing the claim instead of inspecting the change is how the quiet-wrong slips through.

Which is why I'd point the summarizer at the diff, not the agent's answer. Same effort, but now it's reading what changed rather than what the agent said about what changed, and it can flag "this also touched a file you didn't mention." That's the difference between a summary of the testimony and a summary of the evidence. Your workflow is the right shape, just feed it the artifact instead of the narration.

Question, since you've clearly tuned the effort-to-stakes ratio: how do you decide an update is "not too serious" before you've reviewed it? That's the chicken-and-egg I keep hitting, you need to know the stakes to pick the review depth, but knowing the stakes kind of requires the review. Curious whether you go by what you asked for, which files it touched, or just gut.

@dannyheng 

Per-agent email is genuinely clever, and the reason it works is smarter than it looks: you didn't build a notification system, you repurposed one that already solves the hard parts. Email is already async, already on your phone, already threaded per sender, already searchable later. Giving each agent an address means each one gets its own thread, so "what happened while I was gone" becomes an inbox you scan instead of a context you reconstruct. You routed around the whole problem with infrastructure that already existed. Respect.

The ceiling it hits is the one email always hits: it's one-directional and it's noisy at scale. The agent can mail you "I'm blocked on this approval," but you can't cleanly mail back "yes, proceed" and have it act, email wasn't built to be an approval channel with a structured response. And per-agent addresses are elegant at three agents and a flood at fifteen, plus a "done" email and a "blocked, need you now" email land in the same inbox with the same weight, so the urgent one drowns. It's a great read-only awareness layer. It strains as a control layer.

But the credentials problem you raised is the actually hard one, and it's a different beast entirely, so I want to be careful not to hand-wave it. Hardcoding secrets or pasting them into a prompt is exactly the sketchy thing everyone does and nobody should, because now the credential lives in context, in logs, in the model's history. The right shape is the agent never holds the secret, it requests an action that needs the credential, and something outside the agent, a broker you control, injects it at execution time and hands back only the result. The agent gets capabilities, not keys. That's adjacent to the approval problem, actually, "agent needs to do a privileged thing" is the same gate whether the privilege is a risky action or a secret, the human or the policy authorizes, the credential never touches the agent.

That's honestly where this whole space is heading and where my head's at with Pushary, the approval gate and the credential gate are the same primitive: the agent asks, a layer it doesn't control decides and supplies. Question, since you've hit the credential wall directly: when an agent needs creds mid-task, is it usually the same few services over and over, or genuinely unpredictable each time? Because if it's a known set, you can pre-broker those and the agent never has to ask, but if it's unpredictable, you need a real-time hand-off-with-approval flow, and those are very different things to build.

@rahulbhavsar 

"Without that trail, the agent may be productive, but the human still carries all the risk" is the sentence that should anchor this whole category, because it names the asymmetry everyone feels and few articulate. The agent gets the productivity, the human keeps the liability, and visibility is the only thing that closes that gap. It's not a convenience feature, it's how the risk gets transferred back to where the authority actually is. An invisible productive agent is just a way to launder accountability away from the person who'll answer for it.

And you're right that this generalizes past code, but the sharper version of your point is that your three layers are domain-independent while their contents aren't. Intent, change, and approval-point is the universal skeleton, that's true whether it's an agent writing code or updating a CRM. What swaps out is the artifact: plans/diffs/tests for code, CRM-updates/customer-drafts/escalation-reasons for business. So the visibility layer that wins isn't coding-specific or CRM-specific, it's the one that nails the skeleton and lets the artifact type plug in. Most tools are building the skeleton hardcoded to one domain, which is why they don't travel.

Your middle layer is the one I'd argue carries the most weight, too: "what it actually changed," emphasis on actually. Intent is the agent's claim about what it'll do, approval is your decision, but the change is the ground truth, and it's the only one of the three that can't lie. An agent can intend well and act badly. So the trail has to privilege the diff over the narration, what happened, not what the agent said would happen.

That's exactly the shape I'm building Pushary around, the universal skeleton with the coding artifacts plugged in first, intent, real diff, approval point, delivered so the human reclaims the risk instead of carrying it blind. Question, since you've clearly thought across domains: of your three layers, which is hardest to capture honestly for business agents? For code it's mechanical, diffs and tests are concrete. But "what a CRM agent actually changed" or "why it escalated" feels squishier and easier to fake, and I'm curious whether the trail is genuinely harder to trust the further you get from code, or if that's just my coding bias talking.

@blah_mad 

Direct answer: persisted, as part of the run record, not just used to unblock and discarded. And I'd argue the discard-after-unblock version isn't even a lesser version of the product, it's a different product that happens to share a button. An approval you only use to release the session is a keystroke, it does its job and evaporates. An approval you persist, with who decided, when, at what scope, against which proposed action, is a record you can answer for later. The first fixes flow. The second is the only one that touches trust, which is exactly the line you drew.

Because you're right that notifications alone fix timing, not trust, and the reason is structural: a notification is ephemeral by nature. It fires at the moment of need and then it's gone, so a pure notify tool inherits amnesia, it tells you faster and remembers nothing. The instant the terminal closes, "what was approved and why" is gone, and three weeks later when something breaks you're back to reverse-engineering from vibes. Persisting the approval is what converts a momentary "yes" into a durable "this person authorized this specific thing for this reason," and that's the artifact trust is actually built from.

The piece I'd add, because it's where persisted approvals earn their keep: persist the approval bound to what it authorized, not floating on its own. An approval is a decision about a proposed action, so the record has to hold both, "human approved X" next to "agent then did Y," so you can later check that Y actually matched X. Approve "deploy to staging," agent deploys to prod, the persisted-and-bound approval is the thing that catches the divergence. An approval stored without the action it was attached to is auditable in theory and useless in practice, because you can't tell if the agent honored it.

So the run record I want is a timeline per action: agent proposed this and flagged it risky, human approved at this scope at this time, agent then did exactly this, tests said that. Notification is just the delivery mechanism for the approval step, the persistence is what makes the whole thing trustworthy after the fact.

Question back, since you're clearly thinking about this at the record level: would you want the persisted approval to be queryable as its own thing, "show me everything I approved this week and what happened to it," or is it enough that it lives in the run's history for forensics when something breaks? Because the first is a real trust-calibration tool, you'd actually see your own rubber-stamp rate, and the second is just an audit log, and I keep wondering whether people want the mirror or just the receipts.

@atherkildsen 

Ha, the fact that your boss looked at this and went "I'll just build you a physical device" tells you everything, that's not a niche annoyance, that's a problem painful enough that someone's first instinct is hardware. People don't fabricate gadgets for minor inconveniences. The little-device impulse is the same one that gave this thread a desktop aquarium and a menu-bar edge-glow, the pain keeps independently reinventing the same fix, which is usually the sign of a real market and not just a personal quirk.

The thing I'd gently flag, and it's the ceiling every physical version hits: a device lives on your desk, and the whole pain is the thirty minutes you weren't at your desk. A blinking box is great while you're in the room and useless the moment you walk away, which is exactly when the agent goes quiet on you. Ambient hardware solves "at my desk in another window." It can't solve "away from keyboard," because you have to be looking at it. So the device scratches the itch right up until lunch.

Which is the long way of saying yes, more people are absolutely interested, and the version that travels is the one in your pocket you already carry. Same idea as your boss's gadget, minus the constraint that you have to be standing next to it. Genuine question, since your boss clearly spec'd this in his head: what was the device going to actually do, just light up when an agent needs you, or did he have it answering back somehow? I'm curious what shape the hardware instinct takes, because what people imagine building by hand is usually the cleanest statement of what they actually want.

@jordan_bulk 

We provide this at pushary.com, once you have connected your device you get a notification, you click on it and you see your activity feed across your connected agents - we call it the fleet board, give it a shot.

@adam_outbbo 

Two different moves there, worth pulling apart because one's a real fix and one's a loaded gun.

Bypassing all permissions in the config does solve the waiting problem, by deleting the thing that makes it wait. No approval gate, no stall. But you didn't remove the risk, you removed the brakes. Now the agent never pauses, which is great until the one time it confidently does something you'd absolutely have wanted to catch, deletes the wrong thing, force-pushes, touches prod, and there was no gate to stop it. "Allow everything" trades a time problem for a blast-radius problem, and the blast-radius one is the expensive kind because you find out after. It's the right setting for a sandbox where failure is free, and the wrong one the moment the agent can touch anything you can't cheaply undo.

The better shape isn't all-or-nothing, it's tiered: auto-allow the safe, reversible stuff so you're not babysitting trivia, and gate only the genuinely risky calls, auth, payments, migrations, destructive commands. That way you get the no-waiting speed on 90 percent of actions and keep a human on the 10 percent that can actually hurt you. Bypass-all is that idea with the dial cranked to a place I wouldn't leave it on anything that matters.

On the Cowork custom notifications, you're right that you can wire something up, and that's the DIY version of exactly this. The gap is it's per-tool and lives on that machine, so it doesn't follow you to your phone and doesn't span Claude Code plus Cursor plus Codex in one place. Fine for one tool at your desk, strains the moment you're away or running several.

Question, since you've gone full-bypass: has it ever actually bitten you, an agent doing something unattended you wish it had stopped to ask about? Curious whether bypass-all is working because your tasks are genuinely low-stakes, or whether it's been fine so far and the bad day just hasn't happened yet. The answer tells you whether you want the tiered version or you're honestly fine as-is.

@deebuilds 

The line that matters in yours is "most of the time the agent isn't still working, it's waiting, and I don't realize until later," because that names why checking manually doesn't even save you: from the outside, waiting and working look identical. An open window sitting quiet could be the agent thinking hard or frozen on a yes/no, and you can't tell which without reading into it. So manual checking doesn't actually answer the question you have, it just lets you check more often and still guess wrong. The silence is the problem, and staring at more windows doesn't make silence legible.

That's why "keep the windows open" caps out exactly where you said, the moment there's more than one. It's not that watching is hard, it's that you've become a polling loop, and the interval is always either too often (you're babysitting) or too slow (you find out late). There's no good setting, because no amount of looking turns an ambiguous quiet terminal into a clear signal.

The fix is to stop trying to read the silence and make the agent declare its state instead, working, done, or blocked-and-needs-you, so "needs me" arrives as an actual message rather than something you have to infer from a window that went still. That's the simple thing you're describing, the agent tells you instead of you checking, and it's the whole point of what I'm building.

Question, since you've clearly felt the ambiguity: when you do check a quiet window, how do you currently tell waiting from working, do you read the last line and guess, or do you just poke it and see if it responds? I'm trying to understand how people resolve that ambiguity by hand today, because whatever heuristic you use is exactly the thing the tool needs to detect automatically.

@umairnadeem 

You landed on the right abstraction, and it's worth naming what you actually did: you stopped treating an agent as a chat you watch and started treating it as a job with a state machine. That's the whole unlock. A chat is something you observe continuously and interpret. A job either succeeded, failed, or is blocked, and those are discrete states it can declare rather than states you have to infer from a scrolling terminal. The moment a run has an explicit done-or-blocked, you can build on it. The moment it doesn't, you're stuck reading tea leaves. You didn't add a notification, you gave the run a contract.

And your "push plus a short summary" is the correct second half, because a bare state transition tells you to come back but not into what. Done alone makes you reconstruct the context. Done-with-what-changed lets you pick up cold. The summary is the part that turns the push from an interruption into a handoff, which is the difference between saving you the timing and saving you the reload.

Your three-agent threshold is also the number that keeps coming up in this thread, almost everyone independently hits the wall right there, which tells me it's structural and not personal. One agent, watching is free. Three, watching becomes a polling loop you lose. The job-with-states model is exactly what lets you cross that line without your attention being the bottleneck, because you're receiving state changes instead of allocating a watch budget per terminal.

You've basically described Pushary's core design back to me, jobs with explicit states, push on transition, summary attached, so genuine question: did you build this yourself with hooks and scripts, or stitch it from existing tools? Because if you hand-rolled it, I want to know what was annoying to maintain, and if you found tools that already do it cleanly, I want to know which, since "treat runs as jobs" is the right model and I'm curious how much of it people can get without building it from scratch.

@abdullah_bin_asad 

"Babysitting the thing I bought specifically to not babysit" is the whole genre in one line, and the two failures you named are actually opposites that meet in the same frustration, so they're worth splitting.

The first one, pre-approving three times in the prompt and it still pauses, is the agent ignoring an instruction it should have honored. That happens because prompt-level "you have my approval" isn't a real permission, it's a suggestion the model may or may not weight, and the actual gate lives somewhere the prompt can't reach, a tool-permission layer that doesn't read your reassurances. You're granting approval in the wrong place. It's like telling a locked door "I give you permission to open," the door doesn't parse English, it checks a key. Real pre-authorization has to be set at the config or policy level, not asked for in prose, or the agent keeps stopping no matter how many times you say yes.

The second one, automations refusing to start on policy with zero context, is the inverse, the system blocking and then telling you nothing about why. That's worse than the pause, because at least a pause tells you it's waiting. A silent policy refusal with no reason is the single most enraging state a tool can be in, you can't fix what you can't see, so you sit there guessing which invisible rule tripped. The fix isn't fewer policies, it's policies that explain themselves, "blocked because this touched X," so a refusal is actionable instead of a wall.

Both come down to the same missing thing: legibility. The agent should tell you when it's actually blocked and why, and your real approvals should take effect where the gate actually is so the unnecessary pauses stop happening at all. That combination, pre-authorize the safe stuff for real, and surface the genuine blocks with a reason, is most of what I'm building, because "babysitting the anti-babysitting tool" should not be a sentence anyone has to say.

Question, since you've hit both: when it refuses on policy with no context, is it the coding agent itself doing that, or a platform layer on top? Because the no-explanation refusal smells like a safety filter that's deliberately vague, and those are a different beast than a permission gate, the vague-by-design ones are harder to route around and I'm curious which tool is doing it to you, since not all of them are this opaque.