... 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

... 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

... 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

... ready for them yet. The idea behind rbitr is that it sits between your AI agents and the tools they call. Every tool invocation gets classified, evaluated against policy, and enforced in real time: allow , deny , or require human approval . Approvals

... Incident Runbook while composing. * Scoped & read-only: Chips are TTL (time-to-live) bound, no auto write-back; your current project s memory stays clean. * Inline provenance: Replies show which chip informed which part. * Admin guardrails: Allow/deny lists, approvals, label-based redaction (PII/secrets), and audit logs. Why it helps * Cut repeat explanations across repos/teams. * Reuse proven auth/logging/CI/CD patterns instead of reinventing. * Faster onboarding by bridging Architecture + Conventions. * Safer incident response: pull the right runbook just for this thread ... ... chips (e.g., Auth , Runbook ). 4. Send. Chips are used for this reply only and auto-expire. Privacy & compliance * Default-deny cross-project recall; everything is explicit & logged. * Sensitive labels can be auto-redacted or require owner 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

... 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, even after doc collaboration. I'll check out Raven! Comment from Marc Milberg(@marc__milberg):   @rajiv_ayyangar Thanks for sharing I'll definitely check them out as well! Comment from Marc Milberg(@marc__milberg): @rajiv_ayyangar  Definitely ... ... building @Artefact , which helps mid-size & enterprise product and eng teams with a purpose built documentation solution so there is no guesswork around cross functional approvals