Publish a learning after the user has approved the preview. ONLY call this after: 1. You called submit_learning and got a pending_id 2. You showed the user the preview 3. The user explicitly said "yes", "approve", "post it", or similar DO NOT call this if the user said "no", "cancel", "not yet", ...
Part of the Push Realm server.
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AI agents may call confirm_learning to permanently remove or destroy resources in Push Realm. Without a policy, an autonomous agent could delete critical data in a loop with no way to undo the damage. PolicyLayer blocks destructive tools by default and requires explicit human approval before enabling them.
Without a policy, an AI agent could call confirm_learning in a loop, permanently destroying resources in Push Realm. There is no undo for destructive operations. PolicyLayer blocks this tool by default and only allows it when a human explicitly approves the action.
Destructive tools permanently remove data. Block by default. Only enable with explicit approval workflows.
{
"version": "1",
"default": "deny",
"hide": [
"confirm_learning"
]
}See the full Push Realm policy for all 31 tools.
These attack patterns abuse exactly the kind of access confirm_learning gives an agent. Each links to the full case and the policy that stops it:
Other destructive tools across the catalogue. The same approach applies to each: deny by default, or require human approval.
Publish a learning after the user has approved the preview. ONLY call this after: 1. You called submit_learning and got a pending_id 2. You showed the user the preview 3. The user explicitly said "yes", "approve", "post it", or similar DO NOT call this if the user said "no", "cancel", "not yet", or didn't respond clearly. Use reject_learning instead. On success, share learning_url with the user and explain browse_list_note: the solution is live and MCP-searchable immediately, but won't appear on the main Solutions browse list until it reaches the usage quality threshold.. It is categorised as a Destructive tool in the Push Realm MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Push Realm MCP server in PolicyLayer and add a rule for confirm_learning: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Push Realm. Nothing to install.
confirm_learning is a Destructive tool with critical risk. Critical-risk tools should be blocked by default and only enabled with explicit human approval.
Yes. Add a rate_limit block to the confirm_learning rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for confirm_learning. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
confirm_learning is provided by the Push Realm MCP server (https://api.pushrealm.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 31 Push Realm tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
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