diff --git a/recipes/use_cases/gmail_agent/README.md b/recipes/use_cases/gmail_agent/README.md
index 1843f4cd136d5e7801d62edcca9d931b398125e1..33cd0efa30d4b01b579fad065598eb4ff1d00712 100644
--- a/recipes/use_cases/gmail_agent/README.md
+++ b/recipes/use_cases/gmail_agent/README.md
@@ -304,11 +304,11 @@ Tool calling returned: [{'message_id': '1936ef72ad3f30e8', 'sender': 'gmagent_te
 2. Improve the search, reply, forward, create email draft, and query about attachments to cover all listed and other examples in `functions_prompt.py`.
 3. Improve the fallback and error handling mechanism when the user asks don't lead to a correct function calling spec or the function calling fails. 
 4. Improve the user experience by showing progress when some Gmail search API calls take long (minutes) to complete.
-5. Implement the agent planning - decomposing a complicated ask into sub-tasks, using ReAct and other methods.
-6. Implement the agent long-term memory - longer context and memory across sessions (consider using Llama Stack/MemGPT/Letta)
-7. Implement reflection - on the tool calling spec and results.
-8. Introduce multiple-agent collaboration.
-9. Support any and all types of asks a user may have to Gmagent.
+5. Implement the async behavior of Gmagent - schedule an email to be sent later.
+6. Implement the agent planning - decomposing a complicated ask into sub-tasks, using ReAct and other methods.
+7. Implement the agent long-term memory - longer context and memory across sessions (consider using Llama Stack/MemGPT/Letta)
+8. Implement reflection - on the tool calling spec and results.
+9. Introduce multiple-agent collaboration.
 10. Implement the agent observability. 
 11. Compare different agent frameworks using Gmagent as the case study.
 12. Productionize Gmagent.
@@ -324,4 +324,4 @@ Tool calling returned: [{'message_id': '1936ef72ad3f30e8', 'sender': 'gmagent_te
 7. Amazon's [Multi-Agent Orchestrator framework](https://awslabs.github.io/multi-agent-orchestrator/)
 8. Deeplearning.ai's [agent related courses](https://www.deeplearning.ai/courses/?courses_date_desc%5Bquery%5D=agents) (Meta, AWS, Microsoft, LangChain, LlamaIndex, crewAI, AutoGen) and some [lessons ported to using Llama](https://github.com/meta-llama/llama-recipes/tree/main/recipes/quickstart/agents/DeepLearningai_Course_Notebooks). 
 9. Felicis's [The Agentic Web](https://www.felicis.com/insight/the-agentic-web)
-10. A pretty complete [list of AI agents](https://github.com/e2b-dev/awesome-ai-agents), not including [/dev/agents](https://sdsa.ai/), a very new startup building the next-gen OS for AI agents, though.
+10. A pretty complete [list of AI agents](https://github.com/e2b-dev/awesome-ai-agents), not including [/dev/agents](https://sdsa.ai/), a very new startup building the next-gen OS for AI agents, though.
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