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NullRun
Runtime Authorization for AI Agents - Before They Execute
NullRun authorizes AI agent actions at runtime. Each action is evaluated as allowed, requires human approval, or blocked, with evidence recorded for every decision. Python SDK, hosted control plane, approvals, RBAC, traces, audit trail, and tenant isolation are live. Self-serve and deploys in minutes.
Hey Product Hunt 👋 I'm Anatolii, the solo founder behind NullRun.
I didn't start with a big "AI governance" thesis. I started with a much more annoying engineering question: if an agent is allowed to use a tool, who actually gets the final say when it's time to execute?
NullRun sits right at that execution boundary - a control point that checks an agent's tool call before it actually runs, instead of trusting the agent to decide for itself. The first version was literally a Python decorator around one function. I kept pushing the same idea closer to the real point of execution until it became a product.
What interests me now isn't whether agents can make mistakes - we already know they can. It's what happens when an agent has enough access for a mistake to actually matter.
I'm also building this solo, so I'd genuinely rather learn from people here than just talk about what I built.
If you're working with agents: what's one action you'd trust an agent to request, but still wouldn't trust it to execute on its own? Real cases welcome, especially the messy ones.
About NullRun on Product Hunt
“Runtime Authorization for AI Agents - Before They Execute”
NullRun was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #154 on the daily leaderboard. NullRun authorizes AI agent actions at runtime. Each action is evaluated as allowed, requires human approval, or blocked, with evidence recorded for every decision. Python SDK, hosted control plane, approvals, RBAC, traces, audit trail, and tenant isolation are live. Self-serve and deploys in minutes.
On the analytics side, NullRun competes within Developer Tools, Artificial Intelligence and Security — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how NullRun performed against the three products that launched closest to it on the same day.
Who hunted NullRun?
NullRun was hunted by Anatolii Maltsev. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of NullRun including community comment highlights and product details, visit the product overview.
Anatolii Maltsev
Hey Product Hunt 👋 I'm Anatolii, the solo founder behind NullRun.
I didn't start with a big "AI governance" thesis.
I started with a much more annoying engineering question: if an agent is allowed to use a tool, who actually gets the final say when it's time to execute?
NullRun sits right at that execution boundary - a control point that checks an agent's tool call before it actually runs, instead of trusting the agent to decide for itself. The first version was literally a Python decorator around one function. I kept pushing the same idea closer to the real point of execution until it became a product.
What interests me now isn't whether agents can make mistakes - we already know they can.
It's what happens when an agent has enough access for a mistake to actually matter.
I'm also building this solo, so I'd genuinely rather learn from people here than just talk about what I built.
If you're working with agents: what's one action you'd trust an agent to request, but still wouldn't trust it to execute on its own? Real cases welcome, especially the messy ones.