diff --git a/tutorials/50_Using_Pre_Built_Agents_from_Agent_Pack.ipynb b/tutorials/50_Using_Pre_Built_Agents_from_Agent_Pack.ipynb
index 20f3ab7e..3603c04a 100644
--- a/tutorials/50_Using_Pre_Built_Agents_from_Agent_Pack.ipynb
+++ b/tutorials/50_Using_Pre_Built_Agents_from_Agent_Pack.ipynb
@@ -28,7 +28,7 @@
     "There are three ways to use an agent from the pack:\n",
     "\n",
     "- **Run it as is.** Call the `create_*` function, pass a question, and get an answer. The defaults are chosen to work out of the box.\n",
-    "- **Customize it.** Each entry point exposes keyword arguments for swapping models, adding tools, and tuning behavior.\n",
+    "- **Customize it.** Each entry point exposes keyword arguments for the choices specific to that agent, such as which models to use. For everything else, such as adding tools or hooks, use [`clone()`](https://docs.haystack.deepset.ai/docs/agent#cloning-and-modifying-an-agent) on the returned agent.\n",
     "- **Copy it.** Read the implementation and adapt it as a blueprint for your own architecture.\n",
     "\n",
     "### Why these two agents?\n",
@@ -242,12 +242,11 @@
    "source": [
     "### Customizing the agent\n",
     "\n",
-    "Everything is configured through keyword arguments. Only `document_store` and `retriever` are required. A few useful ones:\n",
+    "Everything is configured through keyword arguments. Only `document_store` and `retriever` are required. The other keyword arguments cover the choices specific to this agent: models, the prompt, and limits. A few useful ones:\n",
     "\n",
     "- `llm`: the chat generator that drives the agent loop. Defaults to `OpenAIResponsesChatGenerator(\"gpt-5.4\")` with low reasoning effort. Swap in any tool-calling generator, from OpenAI or another provider.\n",
     "- `max_agent_steps`: caps the loop (default `20`). If the loop is cut off before an answer is written, a built-in `BackupAnswerHook` makes one extra call to produce a best-effort answer, so `last_message` always carries text.\n",
     "- `max_fetched_docs`: how many documents `fetch_documents_by_filter` shows per call (default `10`).\n",
-    "- `extra_tools`, `state_schema`, `hooks`: extend the agent with your own tools, state, and hooks.\n",
     "\n",
     "Here we swap the LLM for a smaller, widely available model and tighten the step budget:"
    ]
@@ -277,6 +276,15 @@
    "id": "41b3360b",
    "metadata": {},
    "source": [
+    "**Extending the agent.** To change anything else, such as adding tools, [hooks](https://docs.haystack.deepset.ai/docs/hooks), or [`State`](https://docs.haystack.deepset.ai/docs/state) entries, use [`clone()`](https://docs.haystack.deepset.ai/docs/agent#cloning-and-modifying-an-agent) on the returned agent. Unpack the existing values so you keep the built-in tools and the `BackupAnswerHook`:\n",
+    "\n",
+    "```python\n",
+    "customized = rag_agent.clone(\n",
+    "    tools=[*rag_agent.tools, my_tool],\n",
+    "    hooks={**rag_agent.hooks, \"before_llm\": [my_hook]},\n",
+    ")\n",
+    "```\n",
+    "\n",
     "**Using a retrieval pipeline.** To use a multi-component retrieval flow (for example hybrid retrieval), pass a `Pipeline` as `retriever` and supply `retrieval_pipeline_input_mapping` (mapping the tool's `query` and `filters` to your pipeline's input sockets) and, optionally, `retrieval_pipeline_output_mapping`. See the [Advanced RAG Agent docs](https://docs.haystack.deepset.ai/docs/advanced-rag-agent) for a full hybrid-retrieval example.\n",
     "\n",
     "**Using the tools on their own.** The four document-store-backed tools are exported individually and bundled as `DocumentStoreToolset`, so you can drop them into your own `Agent` with your own prompt, treating the pack as a toolbox rather than a finished agent:\n",
@@ -320,12 +328,13 @@
    "outputs": [],
    "source": [
     "from haystack_integrations.agent_pack import create_deep_research_agent\n",
+    "from haystack_integrations.tools.tavily import TavilyWebSearchTool\n",
     "\n",
     "research_agent = create_deep_research_agent(\n",
     "    max_subtopics=2,  # delegate at most 2 sub-questions (breadth)\n",
     "    max_concurrent_researchers=2,  # run at most 2 sub-researchers at once\n",
     "    max_researcher_steps=6,  # cap each sub-researcher's search/read/think loop\n",
-    "    max_search_results=5,  # results per web_search call\n",
+    "    search_tool=TavilyWebSearchTool(top_k=5),  # results per web_search call (default: top_k=10)\n",
     ")"
    ]
   },
@@ -392,10 +401,12 @@
     "\n",
     "Each phase takes its own `ChatGenerator`, so you can mix models by cost and capability, or swap in a different provider entirely:\n",
     "\n",
-    "- `scope_llm`, `orchestrator_llm`, `writer_llm`: default to `OpenAIResponsesChatGenerator(\"gpt-5.4\")` (the heavier reasoning steps).\n",
-    "- `researcher_llm`, `summarizer_llm`: default to `OpenAIResponsesChatGenerator(\"gpt-5.4-mini\")` (run many times, so a cheaper model keeps cost down).\n",
+    "- `llm`, `brief_llm`, `report_llm`: default to `OpenAIResponsesChatGenerator(\"gpt-5.4\")` (the heavier reasoning steps).\n",
+    "- `researcher_llm`, `page_summary_llm`: default to `OpenAIResponsesChatGenerator(\"gpt-5.4-mini\")` (run many times, so a cheaper model keeps cost down).\n",
+    "\n",
+    "The breadth/depth of the investigation is fully tunable: `max_subtopics`, `max_concurrent_researchers`, `max_agent_steps`, `max_researcher_steps`, and `max_page_chars`. The orchestrator's `system_prompt` can also be overridden (the `{{ max_subtopics }}` placeholder is substituted).\n",
     "\n",
-    "And the breadth/depth of the investigation is fully tunable: `max_subtopics`, `max_concurrent_researchers`, `max_orchestrator_steps`, `max_researcher_steps`, `max_search_results`, and `max_content_length`.\n",
+    "The sub-researchers' web search is a `search_tool` you can replace, as we did above to limit results. It defaults to `TavilyWebSearchTool(top_k=10)`, and the researcher prompt refers to it as `web_search`, so a custom tool should keep that name.\n",
     "\n",
     "For example, to make the whole run cheaper you might point every phase at a smaller model:\n",
     "\n",
@@ -404,13 +415,22 @@
     "from haystack_integrations.agent_pack import create_deep_research_agent\n",
     "\n",
     "cheap_agent = create_deep_research_agent(\n",
-    "    scope_llm=OpenAIChatGenerator(model=\"gpt-4o-mini\"),\n",
-    "    orchestrator_llm=OpenAIChatGenerator(model=\"gpt-4o-mini\"),\n",
+    "    llm=OpenAIChatGenerator(model=\"gpt-4o-mini\"),\n",
+    "    brief_llm=OpenAIChatGenerator(model=\"gpt-4o-mini\"),\n",
     "    researcher_llm=OpenAIChatGenerator(model=\"gpt-4o-mini\"),\n",
-    "    summarizer_llm=OpenAIChatGenerator(model=\"gpt-4o-mini\"),\n",
-    "    writer_llm=OpenAIChatGenerator(model=\"gpt-4o-mini\"),\n",
+    "    page_summary_llm=OpenAIChatGenerator(model=\"gpt-4o-mini\"),\n",
+    "    report_llm=OpenAIChatGenerator(model=\"gpt-4o-mini\"),\n",
     "    max_subtopics=3,\n",
     ")\n",
+    "```\n",
+    "\n",
+    "As with the Advanced RAG Agent, to change anything else, such as adding tools or hooks to the orchestrator, use [`clone()`](https://docs.haystack.deepset.ai/docs/agent#cloning-and-modifying-an-agent) on the returned agent, unpacking the existing values to keep the built-in tools and the Scope and Write hooks:\n",
+    "\n",
+    "```python\n",
+    "customized = research_agent.clone(\n",
+    "    tools=[*research_agent.tools, my_tool],\n",
+    "    hooks={**research_agent.hooks, \"before_llm\": [my_hook]},\n",
+    ")\n",
     "```"
    ]
   },