Resolve a pending (llm_batch …) suspension with all N responses at once. (llm_batch COLL (lambda x (llm_query …))) fires ONE suspension carrying all N per-item prompts, instead of N serial llm_query suspensions. Reply with a JSON array of exactly N strings — one response per prompt, in header ord...
AI agents invoke lattice_llm_batch_respond to trigger actions in Lattice. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
This tool resumes/triggers execution of suspended batch operations within the Lattice engine. It drives forward the execution of pending computations by injecting responses, making it an Execute action — it causes the engine to continue processing rather than merely reading or writing data. Misuse could cause incorrect or malicious data to propagate through batch LLM processing pipelines.
From the tool's definition Resolve a pending (llm_batch …) suspension with all N responses at once
Documented attack patterns abuse exactly the kind of access lattice_llm_batch_respond gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Lattice, and nothing reaches the server without passing your rules. This is the rule we recommend for lattice_llm_batch_respond:
{
"version": "1",
"default": "deny",
"tools": {
"lattice_llm_batch_respond": {
"limits": [
{
"counter": "lattice_llm_batch_respond_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}lattice_llm_batch_respond stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
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Resolve a pending (llm_batch …) suspension with all N responses at once. (llm_batch COLL (lambda x (llm_query …))) fires ONE suspension carrying all N per-item prompts, instead of N serial llm_query suspensions. Reply with a JSON array of exactly N strings — one response per prompt, in header order. If the array length ≠ N, the batch stays pending and you can retry with the correct count. Single (llm_query …) suspensions use lattice_llm_respond instead. It is categorised as a Execute tool in the Lattice MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Lattice MCP server in PolicyLayer and add a rule for lattice_llm_batch_respond: 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 Lattice. Nothing to install.
lattice_llm_batch_respond is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the lattice_llm_batch_respond 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 lattice_llm_batch_respond. 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.
lattice_llm_batch_respond is provided by the Lattice MCP server (yogthos/matryoshka). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 15 Lattice tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
15 Lattice tools catalogued and risk-classified — across an index of 42,500+ MCP servers.