... multi-client creative work (video, ad scripts, photography) and feedback always ends up scattered across WhatsApp, email, and Figma comments. Half the time nobody's sure which version actually got approved. Curious how others handle this, especially freelancers and small agencies juggling several clients at once. Is your bottleneck getting clear feedback, keeping versions straight, or something else entirely? Comment from Lisa(@yelyzaveta_kibets): Everyone here is on the sending side, so from the approving

... 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

... ninety percent approvals or better produces a suggestion, and it sits there until you accept it. Until you do, the agent keeps asking. There is no path from past approvals

... 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

... right pane, 20 min gone. Just me? What's your setup notifications, layout tricks, or you've accepted the tax? Comment from Kidder(@hellokidder01): @ashir_murtaza1  That works until you can't predict which task will need approval half the time I think "this one's autonomous" and it stops on the third tool call anyway. Also the discipline itself has a cost: one more rule to remember while you're deep in something else. That ... ... kick off longer tasks before I switch focus, not during Hope this help:) Comment from Kidder(@hellokidder01): @sansa_grey  Same The "universal" part is what got me too I didn't want a different setup for Claude Code vs

... 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

... 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

... authorized, controlled, and auditable? That is why @OpenBox is partnering with @Temporal . The integration brings runtime governance directly into long-running Temporal workflows. Builders can: Enforce policies before executions, activities, and signals run Send sensitive actions for human approval Preserve approvals

... 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

... made them succeed or fail? For those who've sold into small/mid-sized businesses, what's usually the biggest blocker to adoption price, integration effort, or something else? Is there a feature here that seems like a "nice to have" versus something that would actually change behavior? Would appreciate any perspective, whether from experience building similar things or just as someone who's dealt with this as a customer or business owner. Comment from Tommy Kang(@sjtkang): What I'm building ... ... fasalvision.com Comment from Nolan Vu(@nolan_vu): @thenameisarian  Interesting, alternative

... tasks or wait on your approval. Sounds like this is the solution you're building towards as are we with out current solution called AgentPays ( Agentpays.dev ). Our solution is a security layer for agents to be able to have automatic approval

... tend to do a good job of recording events but not always the reasoning behind them. Take a simple example: An AI-generated report goes through an internal review before being shared with customers or stakeholders. Someone makes edits, approves the final version, and the workflow moves forward. Months later, if you look back, the audit trail will usually tell you when the changes happened, who made them, and which version was approved

... 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

... chat responses could stop streaming partway through Smarter AI chat with multi-step actions You can now ask the AI to do several things at once (for example, "create three tags and assign them to all my cards") and approve or reject each part of the plan individually. Approving

... Product Hunt. I work in legal ops and compliance, mostly around data workflows, vendor reviews, approvals, and internal risk controls. I'm interested in products that help teams move faster without losing context: clear ownership, audit trails, retention logic, approval

... first command post for running AI agents like an actual squad. Instead of treating each AI tool as a separate island, Loadout gives you one place to: build a roster of agents assign each one a role and domain approve actions before they run keep shared memory in plain Markdown see what changed, what s pending, and what needs your attention switch between providers without losing the whole operation The mental model is less chatbot and more mission control ... ... agent might be your researcher. Another your code operator. Another handles memory. Another sits on approvals

... 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 ... ... hope there could be an AI customer service that makes it easier for me to get answers to such trivial questions. Comment from Konrad S.(@konrad_sx): @rohanrecommends only made 1 post recently (this morning at 9:00, approved