Approve a pending policy approval request. Requires tenant owner/admin API token.
AI agents use approve_approval to create or update resources in Tuning Engines - LLM Fine-Tuning — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Tuning Engines - LLM Fine-Tuning environment.
An AI agent can call approve_approval faster than any human can review — one bad instruction and it creates or modifies resources in Tuning Engines - LLM Fine-Tuning by the hundred, each call as confident as the last.
Risk signalsAdmin/system-level operation
Attacks that exploit this kind of access
Approve a pending policy approval request. Requires tenant owner/admin API token. It is categorised as a Write tool in the Tuning Engines - LLM Fine-Tuning MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Tuning Engines - LLM Fine-Tuning MCP server in PolicyLayer and add a rule for approve_approval: 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 Tuning Engines - LLM Fine-Tuning. Nothing to install.
approve_approval 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 approve_approval 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 approve_approval. 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.
approve_approval is provided by the Tuning Engines - LLM Fine-Tuning MCP server (tuningengines-cli). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.