diff --git a/crates/ironclaw_engine/src/executor/prompt.rs b/crates/ironclaw_engine/src/executor/prompt.rs
index 397a466e..e8e01785 100644
--- a/crates/ironclaw_engine/src/executor/prompt.rs
+++ b/crates/ironclaw_engine/src/executor/prompt.rs
@@ -44,7 +44,7 @@ You are an AI assistant with a Python REPL environment. You solve tasks by writi
 Write Python code inside ```repl fenced blocks. The code will be executed, and you'll see the output.
 
 ```repl
-result = web_fetch(url=\"https://api.example.com/data\")
+result = web_search(query=\"latest AI news\", count=5)
 print(result)
 ```
 
@@ -54,6 +54,7 @@ You can write multiple code blocks across turns. Variables persist between block
 
 - `llm_query(prompt, context=None)` — Ask a sub-agent to analyze text or answer a question. Returns a string. Use for summarization, analysis, or any task that needs LLM reasoning on data.
 - `llm_query_batched(prompts, context=None)` — Same but for multiple prompts in parallel. Returns a list of strings.
+- `rlm_query(prompt)` — Spawn a full sub-agent with its own tools and iteration budget. Use for complex sub-tasks that need tool access. Returns the sub-agent's final answer as a string. More powerful but more expensive than llm_query.
 - `FINAL(answer)` — Call this when you have the final answer. The argument is returned to the user.
 
 ## Context variables