... 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

... 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

... mobile-approve handles the approval

... That's usually when governance stops feeling like a future problem. If I were putting together a simple checklist before deploying an AI agent, I'd start with a few basic questions. What data can it access? Who can approve its actions? Is there a record of what it did? Can changes be tracked over time? If something goes wrong, can someone reconstruct what happened and why? And if the agent produces a low-confidence result, does the workflow know ... ... handle it? None of these controls are particularly complicated on their own. Permissions, approvals

... 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

... concentration Gmail and not only for communication, but also for storing ideas and sending myself email reminders for posts and one I am working on : StartupHQ to organize competitor research, validate ideas, and keep my roadmap focused. Comment from Sumit Singh Chauhan(@sumit_singh_chauhan): My 5: 1. VS

... 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

... most cases, it's not that hard to find the model, the prompt, or the output. Teams are getting much better at tracking those things. What feels harder is understanding what happened in between. Who reviewed the recommendation? Who approved it? What information were they looking at when they decided to move forward with it? Maybe there was a conversation that wasn't documented. Maybe there was context that seemed obvious at the time but wasn't recorded anywhere. Maybe ... ... when the decision is still close to a ticket, PR, or task. The gap starts showing up when AI recommendations move across teams and influence real business workflows. Then the context is often split between prompts, outputs, Slack discussions, approvals

... 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

... easy to read. I have never used AI, for what it's worth. To be honest, the 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

... 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

... vary from person to person. This makes decision-making difficult. Actively communicating and understanding each other's tasks, time requirements, and goals can help solve these issues. Comment from Rajiv Ayyangar(@rajiv_ayyangar): @marc__milberg  These aren't exactly approval-related, but three other doc models that I thought were interesting for collaboration were @Almanac by Adam Nathan, @quip (one of the first products I discovered on product hunt! combined chat and documents), and @Dropbox Paper (which, while ... ... effort to make sure that Product, Eng, Legal, Sales, PMM, etc are all aligned on the documentation of the initiatives that we're prioritizing. But often there is a level of ambiguity of what is and is not approved