docs: detail dual-phase incremental persistence in README

- Clarified that inventory data is saved without waiting for workflow completion.
- Specified save intervals for both Metadata (500 links) and AI (20 batches) phases.

README.md

@@ -705,7 +705,10 @@ The heart of the new Nubenetes is a suite of AI Agents that operate on our `deve
 4.  **Resilient Architecture Core**:
     - **Exponential Backoff**: Intelligent `tenacity`-based retry logic in `gemini_utils.py` gracefully handles 429 Rate Limits before triggering the Circuit Breaker.
     - **Flash-First Architecture**: Prioritizes Gemini Flash/Lite models for high-density Analyst tasks, enabling processing of 10,000+ resources within the 6-hour GitHub Actions limit through 100-item batching and 2-second safety delays.
-    - **Incremental Persistence (Mandate 22)**: Implements an auto-save mechanism that flushes the `inventory.yaml` database to disk every 20 batches. This ensures that progress is never lost even if a workflow run is interrupted or cancelled.
+    - **Incremental Persistence (Mandate 22)**: Implements a dual-phase auto-save mechanism that flushes the `inventory.yaml` database to disk periodically **without waiting for the workflow to finish**:
+        *   **Metadata Phase**: Saves every **500 GitHub repositories** processed.
+        *   **AI Phase**: Saves every **20 AI batches** (1,000 resources) analyzed.
+        *   **Cache Safety**: Utilizes `actions/cache` with an `always()` save condition, ensuring that even if a run is cancelled or hits the 6-hour timeout, the next run resumes exactly where the previous one left off.
     - **Fast-Track Sequential Model**: Optimized for stability and speed, bypassing the complexity of distributed systems and leveraging the pre-computed metadata from the inventory.
     - **Pip Caching**: All workflows utilize `cache: pip` for lightning-fast execution and reduced compute costs.
     - **AI PR Guardian**: Enforces the `PULL_REQUEST_TEMPLATE.md` checklist automatically on community contributions.