docs: custom output extraction (#817)

In deep agent workflows, each sub‐agent automatically performs an LLM
step to summarize its tool calls before returning to its parent. This
leads to:
1. Excessive latency: every nested agent invokes the LLM, compounding
delays.
2. Loss of raw tool data: summaries may strip out details the top‐level
agent needs.

We discovered that `Agent.as_tool(...)` already accepts an
(undocumented) `custom_output_extractor` parameter. By providing a
callback, a parent agent can override what the sub‐agent returns e.g.
hand back raw tool outputs or a custom slice so that only the final
agent does summarization.

---

This PR adds a “Custom output extraction” section to the Markdown docs
under “Agents as tools,” with a minimal code example.

docs/tools.md

@@ -284,6 +284,33 @@ async def run_my_agent() -> str:
     return str(result.final_output)
 ```
 
+### Custom output extraction
+
+In certain cases, you might want to modify the output of the tool-agents before returning it to the central agent. This may be useful if you want to:
+
+- Extract a specific piece of information (e.g., a JSON payload) from the sub-agent's chat history.
+- Convert or reformat the agent’s final answer (e.g., transform Markdown into plain text or CSV).
+- Validate the output or provide a fallback value when the agent’s response is missing or malformed.
+
+You can do this by supplying the `custom_output_extractor` argument to the `as_tool` method:
+
+```python
+async def extract_json_payload(run_result: RunResult) -> str:
+    # Scan the agent’s outputs in reverse order until we find a JSON-like message from a tool call.
+    for item in reversed(run_result.new_items):
+        if isinstance(item, ToolCallOutputItem) and item.output.strip().startswith("{"):
+            return item.output.strip()
+    # Fallback to an empty JSON object if nothing was found
+    return "{}"
+
+
+json_tool = data_agent.as_tool(
+    tool_name="get_data_json",
+    tool_description="Run the data agent and return only its JSON payload",
+    custom_output_extractor=extract_json_payload,
+)
+```
+
 ## Handling errors in function tools
 
 When you create a function tool via `@function_tool`, you can pass a `failure_error_function`. This is a function that provides an error response to the LLM in case the tool call crashes.