Create a new AI filter for semantic intent-based message matching. AI filters use vector embeddings (via Voyage AI) to detect whether an incoming message matches a specific intent or topic. The filter's description is embedded as a reference vector at creation time. When a message arrives, its em...
Part of the Dialogbrain server.
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AI agents use ai_filters_create 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 ai_filters_create 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": {
"ai_filters_create": {
"limits": [
{
"counter": "ai_filters_create_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 ai_filters_create 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.
Create a new AI filter for semantic intent-based message matching. AI filters use vector embeddings (via Voyage AI) to detect whether an incoming message matches a specific intent or topic. The filter's description is embedded as a reference vector at creation time. When a message arrives, its embedding is compared against this reference using cosine similarity. The description field is the most important part — it becomes the reference embedding that all incoming messages are compared against. Write it as a clear statement of what kind of messages should match: - 'Customer asking about pricing, subscription plans, or billing' - 'User reporting a bug, crash, or unexpected behavior in the product' - 'Inbound sales lead expressing interest in purchasing or trialing' The threshold controls sensitivity: 0.5 is a balanced default, lower values (0.3) cast a wider net, higher values (0.8) require closer matches. Note: This tool calls the Voyage AI embedding API to generate the reference vector.. 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 ai_filters_create: 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.
ai_filters_create 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 ai_filters_create 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 ai_filters_create. 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.
ai_filters_create 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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