adding summary and faq and then tweaking with skills

Author: anupras-mohapatra-arm

content/learning-paths/cross-platform/ai-agent-cpu-orchestration/_index.md

@@ -17,9 +17,54 @@ prerequisites:
     - An Arm-based computer running Linux or macOS, such as an Apple silicon MacBook or an NVIDIA DGX Spark
     - Familiarity with running Python scripts from the terminal
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-07-10T21:49:51Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 59eaec53e94f78a294d2d04f28d81bd885d0d47f30e88d3c87230145bac77980
+  summary_generated_at: '2026-07-10T21:49:51Z'
+  summary_source_hash: 59eaec53e94f78a294d2d04f28d81bd885d0d47f30e88d3c87230145bac77980
+  faq_generated_at: '2026-07-10T21:49:51Z'
+  faq_source_hash: 59eaec53e94f78a294d2d04f28d81bd885d0d47f30e88d3c87230145bac77980
+  summary: >-
+    You'll run a local concierge agent on an Arm machine and examine how the workload
+    splits between CPU orchestration and GPU-backed model inference. You'll set up a
+    Python environment, configure a Serper web search API key, and serve Gemma 3 locally
+    with Ollama. As the agent answers your research-style questions, you'll use its per-query
+    timeline to see how much of web search, scraping, ranking, deduplication, and extraction runs
+    on the CPU.
+  faqs:
+  - question: How do I know Ollama is ready before running the agent?
+    answer: >-
+      Confirm Ollama is running and serving the `gemma3:4b` model. The local API at `http://localhost:11434`
+      should be available before starting the script.
+  - question: What do I need in the terminal environment when I start the script?
+    answer: >-
+      Activate your Python virtual environment and set `SERPER_API_KEY` in the same terminal session.
+      The agent reads this variable at runtime for web search.
+  - question: What result should I expect when the agent starts correctly?
+    answer: >-
+      You should see a greeting that explains the agent browses websites in real time and a prompt
+      asking for your question. Type `quit` or `exit` to end the session.
+  - question: How do I read the CPU and GPU timeline the agent prints?
+    answer: >-
+      CPU entries in the timeline correspond to tools (web search, scraping) and the orchestration pipeline (query expansion,
+      ranking, deduplication, extraction). GPU entries correspond to the Gemma model running through
+      Ollama. The agentic chain interleaves these steps.
+  - question: Where can I see how the workflow is organized in the code?
+    answer: >-
+      Open `concierge_agent.py`. It groups the tools, the LLM "brain," the orchestration pipeline,
+      and the agentic chain, and indicates which components run on the CPU or GPU.
+# END generated_summary_faq
+
 author: Jaidev Singh Chadha
 
-generate_summary_faq: true
+generate_summary_faq: false
 rerun_summary: false
 rerun_faqs: false
 
@@ -67,3 +112,4 @@ weight: 1                       # _index.md always has weight of 1 to order corr
 layout: "learningpathall"       # All files under learning paths have this same wrapper
 learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
 ---
+