Save a behavioral rule, preference, or correction that should guide future agent behavior. Use this when the user gives explicit guidance like 'always reply in Russian', 'don't suggest meetings before 11am', or 'invoice link goes via email, not chat'. Structure the rule as: the rule itself, why i...
Part of the Dialogbrain server.
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AI agents use feedback_save to create or modify resources in Dialogbrain. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.
Without a policy, an AI agent could call feedback_save repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach Dialogbrain.
Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.
{
"version": "1",
"default": "deny",
"tools": {
"feedback_save": {
"limits": [
{
"counter": "feedback_save_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}See the full Dialogbrain policy for all 157 tools.
These attack patterns abuse exactly the kind of access feedback_save gives an agent. Each links to the full case and the policy that stops it:
Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Save a behavioral rule, preference, or correction that should guide future agent behavior. Use this when the user gives explicit guidance like 'always reply in Russian', 'don't suggest meetings before 11am', or 'invoice link goes via email, not chat'. Structure the rule as: the rule itself, why it matters (if stated), and how to apply it. Scope: 'workspace' for org-wide rules, 'agent' for per-agent overrides, 'person' for per-contact preferences. Prefer feedback.save over notes.save for anything that's instructive rather than informational.. It is categorised as a Write tool in the Dialogbrain MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Dialogbrain MCP server in PolicyLayer and add a rule for feedback_save: 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 Dialogbrain. Nothing to install.
feedback_save is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the feedback_save 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 feedback_save. 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.
feedback_save is provided by the Dialogbrain MCP server (https://api.dialogbrain.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 157 Dialogbrain tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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