diff --git a/libs/python/agent/agent/callbacks/prompt_instructions.py b/libs/python/agent/agent/callbacks/prompt_instructions.py
new file mode 100644
index 00000000..e7cf67fc
--- /dev/null
+++ b/libs/python/agent/agent/callbacks/prompt_instructions.py
@@ -0,0 +1,47 @@
+"""
+Prompt instructions callback.
+
+This callback allows simple prompt engineering by pre-pending a user
+instructions message to the start of the conversation before each LLM call.
+
+Usage:
+
+    from agent.callbacks import PromptInstructionsCallback
+    agent = ComputerAgent(
+        model="openai/computer-use-preview",
+        callbacks=[PromptInstructionsCallback("Follow these rules...")]
+    )
+
+"""
+
+from typing import Any, Dict, List, Optional
+
+from .base import AsyncCallbackHandler
+
+
+class PromptInstructionsCallback(AsyncCallbackHandler):
+    """
+    Prepend a user instructions message to the message list.
+
+    This is a minimal, non-invasive way to guide the agent's behavior without
+    modifying agent loops or tools. It works with any provider/loop since it
+    only alters the messages array before sending to the model.
+    """
+
+    def __init__(self, instructions: Optional[str]) -> None:
+        self.instructions = instructions
+
+    async def on_llm_start(self, messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        # Pre-pend instructions message
+        if not self.instructions:
+            return messages
+
+        # Ensure we don't duplicate if already present at the front
+        if messages and isinstance(messages[0], dict):
+            first = messages[0]
+            if first.get("role") == "user" and first.get("content") == self.instructions:
+                return messages
+
+        return [
+            {"role": "user", "content": self.instructions},
+        ] + messages