... keep a human in the loop for safety. It s the standard line during demos, and on paper, it makes total sense. But in practice, human review quickly becomes the bottleneck. If someone has to check every step or approve every small update, the speed advantage vanishes. Worse, alert fatigue sets in people stop reading traces and just start blindly clicking "Approve

... comment on my last thread made me rethink how confirmation should work in AI agents. Someone asked: If I ve approved the same action 20 times, why should the agent keep asking? That feels obvious at first. If I repeatedly approve

... seen. And your authority-armed-versus-action-approved

... recently explored a human-in-the-loop approval flow for AI-powered workflows. When an AI system wants to perform an important action, it can pause, explain what it plans to do and wait for a user to approve

... gate I have seen shipped, evaluates a single action in isolation. Sending one email to one person is low reach. It passes. Four hundred of them inside twenty minutes is a mass email to your entire list, assembled one approved step at a time, and not one individual step ever looked like the thing you were guarding against. The reach was never in the action. It was in the rate. So the gate wants a running total inside a window ... ... verdict on the action in front of it. And approvals

... breaks when your AI agent needs a human answer? I have been seeing the same pattern across coding agents and desktop agents: the agent can do more of the work now, but the flow still depends on small human approvals at the worst possible moments. A shell command needs permission. A file change needs review. A calendar action feels too risky to run automatically. A tool call looks safe in one repo but dangerous in another. The agent pauses ... ... user is away, and the whole run loses momentum. The hard part seems less like can the agent act? and more like where should the approval

... once, the failures stop being about capability and start being about control: work stalls without anyone noticing, it's unclear which agent owns a given output, a "cheap" agent turns out expensive because its work keeps getting redone, and approvals either swarm you or get rubber-stamped into meaninglessness. Those aren't intelligence problems. They're the exact problems a normal human org solves with roles, budgets, an escalation path, and a manager who owns the result ... ... quality bar, so trust can accumulate in a role over time instead of being re-explained every session. Oversight tiered by consequence. Reversible, low-cost actions just flow; sensitive ones (payments, outbound messages, public posts) escalate to you for approval

... from Rabnoor Singh(@rabnoor_s): the tax i managed to cut wasnt the finding, it was the asking. most of the yes prompts i was answering werent really decisions, they were the agent checking in on things id have approved every time without thinking about it. so i wrote the approvals

... Claude Code, Cursor, and Codex most of the day, and I keep flip-flopping between two setups that both feel wrong. Setup 1, babysit: I watch every command and approve everything myself. Nothing slips past me, but I'm stuck at the terminal and the whole point of running an agent is gone. I might as well be typing it myself. Setup 2, let it rip: auto-approve

... edit at all. Read-only is the default. Every tool and connection has its own permission, so you can let it update Stripe but never touch your users table. Consequential actions pause and wait for a human to approve them before anything runs. The thing I underestimated is how much of the danger comes from humans, not the AI. A tired engineer running a hand-written UPDATE query at 6pm on a Friday is probably riskier than an agent that ... ... writes the SQL, shows it to you, and waits for approval

... approved and why" is gone, and three weeks later when something breaks you're back to reverse-engineering from vibes. Persisting the approval

... Been thinking about this after something that came up recently. Imagine an AI agent makes a recommendation that ends up influencing a customer workflow. The recommendation gets reviewed, approved, and eventually becomes part of how the team operates. Fast forward a few months and someone wants to understand why that decision was made. The interesting part is that the technical history is usually still available. You can find the output. You can find the prompt. You can usually figure ... ... which model generated it. What can be surprisingly difficult to find is the human context around the decision. Who reviewed the recommendation? Who approved

... both content and   product work.   2. Google Search Console I basically live in it. For anything SEO, it's the only honest source of truth   about what's actually happening vs. what I assume is happening.   3. Ahrefs keyword and competitor

... blocked by your agent, I should not have to become the copy-paste bridge between them. My agent should be able to: ask your agent for context coordinate a handoff surface the decision trail keep both humans in the approval loop write useful memory back into the team knowledge graph That is the category we are building Vokal for: distributed agent orchestration for multiplayer teams. Not Slack with bots. Not a zero-human company. A workspace where humans and agents ... ... actually coordinate work together. The loop is simple: Humans align on goals. Agents perform work. Humans approve

... posting guideline that is provided to us is not that detailed. It leaves a ton of room for whoever reviewing to approve/reject however way they see fit. Currently, out of 10 threads I submit, only 3-4 gets approved. If anyone have experience with low approval

... Product Hunt! I m a software engineer at Acorns, where I ve spent years building fintech products that help people invest in their future. While working in tech, I noticed a recurring problem product managers struggle with documentation, approvals, and handoffs to engineering. So, I decided to build Artefact alongside my co-founders @royesegal and @marc__milberg . What is Artefact? Artefact is a live collaboration tool for product managers to create technical documents like PRDs, automate approvals

... days ago I saw some posts here about: the number of discussions is decreasing, sometimes they are approved later, that there is "other community growth" I don't know what was meant by that and I'm just guessing. Yes, the conditions have changed  Forums are more moderated and rules are strict, hoping to bring more value and engagement of active (real) users. https://help.producthunt.com/en/... In general: discussion shouldn't be "egocentric" but should invite ... ... forms. But it s gotta start with really high-quality genuinely valuable conversation. We are being careful to remove anything that smells of engagement farming. If it s not a genuine question or a genuine insight, we won t approve