... OpenBox.ai is live on Product Hunt today with major product updates! We ve expanded OpenBox from a runtime governance layer into a framework-agnostic control layer that teams can use across their existing AI agent stack. : 8+ integrations with Temporal, LangChain, LangGraph, CrewAI, Mastra, Deep Agents, CopilotKit, and n8n Policy enforcement before agent actions execute Human approval workflows for sensitive actions Real-time monitoring of agent behaviour and risk Complete session replay to understand what happened Tamper ... ... NIST The goal is to give teams one consistent way to control and audit their agents, regardless of the framework or system they use. If you re building AI agents, we d love your feedback on the updated OpenBox

... agents can now run reliably for hours, days, and even across system failures. But keeping an agent running is only half the challenge. How do you ensure every action it takes is 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 across workflow restarts Create cryptographically signed records ... ... governed decisions Add governance without changing existing Temporal workflows Temporal keeps the workflow running. OpenBox

... result, and how the final decision was made. Those questions can come from customers, internal stakeholders, auditors, or simply from the team trying to understand its own systems. A lot of conversations I end up having while building OpenBox revolve around those operational questions rather than legal ones. The underlying theme is usually the same: as AI becomes part of real workflows, people want a clearer understanding of how decisions are made and who is responsible for them ...

... think about records, approvals, ownership, or controls when they're experimenting. Those topics show up when an agent becomes something people depend on. It's a problem I've found myself paying more attention to while building OpenBox, but I suspect every team deploying AI agents eventually runs into the same shift. At what point do you think an AI workflow needs governance? Comment from Syed Noor(@syed_noor4): The line I've landed on: it's not "when ... ... governance later you're rebuilding. So, my answer to "at what point": the moment the agent stops only reading and starts doing. That's usually earlier than the demo makes it feel. Curious how you're drawing the read-vs

... makes a bad call or hallucinates. In practice, they are not just buying automation. They are buying a way to trust the automation. It's one of the reasons I've been thinking a lot about this while building @OpenBox . Agencies and internal teams usually need a clear record of what the agent did, what it touched, and where human approval happened. That tends to matter more once the workflow is real and clients start asking harder questions. Are clients ...

... checks, approval workflows, human oversight, decision records, and governance controls need to exist while the workflow is running, not only after it's complete. This is the area I've spent a lot of time thinking about while building @OpenBox . None of these categories replace each other. Together, they create the control, visibility, and accountability that enterprise AI systems eventually need. What governance category do you think is still missing from the conversation? Comment from Dev Grover(@grover___dev): @desirewateas ...

... demo and becomes part of a real workflow. An agent might read documents, make recommendations, trigger actions, or interact with customers. At that point, understanding control, ownership, and visibility becomes just as important as the model itself. Working on OpenBox, I've noticed that most builders aren't asking for more compliance language. They're usually trying to answer much simpler questions: what can this agent do, who approved it, what changed, and is it still operating within the boundaries ...

... what I'd care about. But then this doesn't differ from how it was before LLMs :) Comment from Dev Grover(@grover___dev): That makes sense @zhen_han  . We ve been thinking about a similar problem with @OpenBox , especially around how agent actions and human approvals sit in the same decision trail. The part I keep coming back to is that logs prove something happened, but they do not always explain why it was trusted. That context around approval ...