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Show HN: AgentBouncr – Governance layer for AI agents

1 points by Soenke_Cramme 8mo ago

Soenke_Cramme 8mo ago
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vincentvandeth 8mo ago
Interesting approach. Runtime enforcement is the part most people skip — they focus on logging what happened but don't prevent bad actions in the first place. The policy engine + kill switch combination makes sense for that. I've been running ~2,400 multi-agent dispatches and came at this from the opposite direction: I started with staging gates (propose → human review → execute) as the runtime layer, then realized I also needed a forensic layer for when things slip through or when I need to understand patterns over time. Curious about a few things: - How granular are the JSON policies in practice? I found that "agent X can use tool Y" breaks down fast when agents chain tools in unexpected ways. The sequence matters more than individual permissions. - The hash-chained audit trail — how do you handle schema evolution? After a few months of production, what you want to log changes significantly. Hash chains make adding fields tricky without breaking the chain. - What happens when an agent crashes mid-action? With the hash chain, do you risk a corrupted tail entry that invalidates subsequent verification? The runtime vs. after-the-fact distinction is important. Ideally you want both — prevent what you can, reconstruct what you couldn't prevent. Nice to see someone tackling the prevention side seriously.
- Soenke_Cramme 7mo ago
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ratnaditya 8mo ago
The hash-chained audit trail is a nice touch — append-only logs are underrated for agent governance. Curious how you handle the policy definition UX for teams without security backgrounds. JSON rules are expressive but require knowing what to block in advance. Most developers don't know their risk profile until after something goes wrong — which is part of why I think automated scanning before policy definition matters. Does AgentBouncr have any way to suggest policies based on what tools the agent actually has access to?
- Soenke_Cramme 7mo ago
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pipejosh 7mo ago
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guillermollopis 7mo ago
The permission control and policy engine angle is interesting, Article 14 (human oversight) explicitly requires the ability for authorized persons to intervene, override, or stop the AI system. Having that built into the agent framework layer makes sense. One thing I'd think about: how does this map to the documentation requirement? Under Annex IV, you need to document exactly what oversight mechanisms exist, how they work, and under what conditions they activate. The governance layer generates the evidence, but someone still needs to turn it into the structured documentation format that regulators expect. This is the gap I see across most of the compliance tooling, runtime enforcement tools (like this) and documentation tools (Holistic AI, Annexa, ComplyAct) solve different halves of the problem. The teams that will have the smoothest conformity assessments are the ones connecting both.

1 points by Soenke_Cramme 8mo ago

[dead]

Interesting approach. Runtime enforcement is the part most people skip — they focus on logging what happened but don't prevent bad actions in the first place. The policy engine + kill switch combination makes sense for that. I've been running ~2,400 multi-agent dispatches and came at this from the opposite direction: I started with staging gates (propose → human review → execute) as the runtime layer, then realized I also needed a forensic layer for when things slip through or when I need to understand patterns over time. Curious about a few things: - How granular are the JSON policies in practice? I found that "agent X can use tool Y" breaks down fast when agents chain tools in unexpected ways. The sequence matters more than individual permissions. - The hash-chained audit trail — how do you handle schema evolution? After a few months of production, what you want to log changes significantly. Hash chains make adding fields tricky without breaking the chain. - What happens when an agent crashes mid-action? With the hash chain, do you risk a corrupted tail entry that invalidates subsequent verification? The runtime vs. after-the-fact distinction is important. Ideally you want both — prevent what you can, reconstruct what you couldn't prevent. Nice to see someone tackling the prevention side seriously.

Soenke_Cramme 7mo ago
[dead]

ratnaditya 8mo ago

The hash-chained audit trail is a nice touch — append-only logs are underrated for agent governance. Curious how you handle the policy definition UX for teams without security backgrounds. JSON rules are expressive but require knowing what to block in advance. Most developers don't know their risk profile until after something goes wrong — which is part of why I think automated scanning before policy definition matters. Does AgentBouncr have any way to suggest policies based on what tools the agent actually has access to?

Soenke_Cramme 7mo ago
[dead]

pipejosh 7mo ago

[dead]

guillermollopis 7mo ago

The permission control and policy engine angle is interesting, Article 14 (human oversight) explicitly requires the ability for authorized persons to intervene, override, or stop the AI system. Having that built into the agent framework layer makes sense. One thing I'd think about: how does this map to the documentation requirement? Under Annex IV, you need to document exactly what oversight mechanisms exist, how they work, and under what conditions they activate. The governance layer generates the evidence, but someone still needs to turn it into the structured documentation format that regulators expect. This is the gap I see across most of the compliance tooling, runtime enforcement tools (like this) and documentation tools (Holistic AI, Annexa, ComplyAct) solve different halves of the problem. The teams that will have the smoothest conformity assessments are the ones connecting both.