I've noticed that AI governance, AI safety, and AI compliance often get used interchangeably. They're related, but I don't think they're describing the same thing.
The simplest way I've found to think about it is that AI safety is about reducing harmful behavior. If an AI system gives dangerous advice, produces harmful outputs, or behaves in unexpected ways, safety is concerned with preventing those outcomes.
AI compliance is different. It focuses on whether a system meets legal, regulatory, or policy requirements. The question is less "is this safe?" and more "are we meeting the rules we're expected to follow?"
AI governance sits in a slightly different place. It's about ownership, visibility, and control. Who can approve an AI workflow? What records exist? Who is responsible when decisions are made? How can a team understand what happened later if something goes wrong?
Hey Product Hunt ๐
Weโre excited to launch the latest version of OpenBox.ai, now with an expanding ecosystem of ๐ด+ ๐ถ๐ป๐๐ฒ๐ด๐ฟ๐ฎ๐๐ถ๐ผ๐ป๐ for governing AI agents wherever they are built and run.
AI agents are gaining access to sensitive data, internal systems, communication tools, and real-world actions. But most teams still cannot clearly answer:
โข What did the agent do?
โข Was it authorized to do it?
โข Which policies were checked?
โข Who approved a sensitive action?
โข Can we prove what happened later?
๐ช๐ฒ ๐ฏ๐๐ถ๐น๐ ๐ข๐ฝ๐ฒ๐ป๐๐ผ๐ ๐๐ผ ๐ด๐ถ๐๐ฒ ๐๐ฒ๐ฎ๐บ๐ ๐๐ต๐ผ๐๐ฒ ๐ฎ๐ป๐๐๐ฒ๐ฟ๐.
๐ข๐ฝ๐ฒ๐ป๐๐ผ๐ ๐ฎ๐ฑ๐ฑ๐ ๐ฎ ๐ฟ๐๐ป๐๐ถ๐บ๐ฒ ๐๐ฟ๐๐๐ ๐น๐ฎ๐๐ฒ๐ฟ ๐๐ผ ๐๐ผ๐๐ฟ ๐ฒ๐ ๐ถ๐๐๐ถ๐ป๐ด ๐ฎ๐ด๐ฒ๐ป๐ ๐๐๐ฎ๐ฐ๐ธ.
It helps you:
โ Enforce policies before agent actions execute
โ Require human approval for sensitive operations
โ Monitor agent behaviour and risk in real time
โ Replay complete agent sessions
โ Maintain tamper-proof audit trails
โ Apply consistent governance across frameworks and systems
โ Map agent activities to regulatory frameworks such as the EU AI Act and NIST
You can add OpenBox ๐๐ถ๐๐ต๐ผ๐๐ ๐ฟ๐ฒ๐ฏ๐๐ถ๐น๐ฑ๐ถ๐ป๐ด ๐๐ผ๐๐ฟ ๐ฒ๐ ๐ถ๐๐๐ถ๐ป๐ด ๐ฎ๐ด๐ฒ๐ป๐ ๐ฎ๐ฟ๐ฐ๐ต๐ถ๐๐ฒ๐ฐ๐๐๐ฟ๐ฒ.
๐ ๐ข๐๐ฟ ๐ถ๐ป๐๐ฒ๐ด๐ฟ๐ฎ๐๐ถ๐ผ๐ป ๐ฒ๐ฐ๐ผ๐๐๐๐๐ฒ๐บ ๐ถ๐ป๐ฐ๐น๐๐ฑ๐ฒ๐:
โข Temporal
โข LangChain
โข LangGraph
โข CrewAI
โข Mastra
โข Deep Agents
โข CopilotKit
โข n8n
๐ข๐๐ฟ ๐ด๐ผ๐ฎ๐น ๐ถ๐ ๐๐ถ๐บ๐ฝ๐น๐ฒ:
Regardless of which framework you use, you should have one consistent way to control what your agents can do and prove what they did.
๐ช๐ฒ ๐๐ผ๐๐น๐ฑ ๐น๐ผ๐๐ฒ ๐๐ผ๐๐ฟ ๐ณ๐ฒ๐ฒ๐ฑ๐ฏ๐ฎ๐ฐ๐ธ:
โข Which agent framework should we integrate with next?
โข What is your biggest concern when moving agents into production?
โข Which governance controls would make you more comfortable giving agents greater autonomy?
Thank you for checking out OpenBox. Weโll be here throughout the launch to answer questions and hear your thoughts! ๐
๐ ๐๐ ๐ฝ๐น๐ผ๐ฟ๐ฒ ๐๐ต๐ฒ ๐ข๐ฝ๐ฒ๐ป๐๐ผ๐ ๐ฑ๐ผ๐ฐ๐๐บ๐ฒ๐ป๐๐ฎ๐๐ถ๐ผ๐ป:
https://docs.openbox.ai/