think-bigger/docs/grok-chat/User Story: Media Processing and Agent Customization.md

User Flow Example: Media Processing and Agent Customization

This document illustrates a critical user journey within the Advanced Second Brain PKM system, demonstrating the seamless integration of media processing, knowledge extraction, and the advanced capability of agent engineering and cross-domain querying.

Scenario: Integrating a New Video Lecture and Customizing the Agent

The following steps detail how a user interacts with the system to ingest new media, extract insights, modify an agent's behavior, and execute a complex, cross-domain query.

Step 1: Data Ingestion via Background Agent

The process begins with the user introducing new knowledge into the system.

1. User Action: The user acquires a new video lecture on neuroscience and places the file into the designated local directory for that domain, specifically within the Neuroscience/Media folder. This action leverages the system's core principle of Local Data Sovereignty.
2. System Action: The system's background "Media Scraper Agent" automatically detects the new file.
3. System Action: The agent initiates a transcription process, generating an interactive transcript file that is placed alongside the video. This transcript is synchronized with the video timeline, preparing the media for advanced analysis.

Step 2: Knowledge Extraction in Knowledge Browser Mode

The user then moves to the application interface to engage with the newly ingested data.

1. User Action: The user navigates to the Neuroscience Domain using the Global Navigation Sidebar.
2. User Action: Within the Knowledge Browser Mode, the user selects the video file from Pane 1: The Drawer.
3. System Action: The video is displayed in Pane 2: Content Viewer, accompanied by the synchronized, interactive transcript.
4. User Action: To extract key insights, the user selects a "Fabric" pattern, such as "Extract Ideas," and clicks the corresponding button in Pane 3: Insight/Fabric.
5. System Action: The Neuroscience Agent processes the transcript, and the right pane is populated with structured, extracted bullet points, representing the key insights from the lecture.

Step 3: Cross-Domain Agent Customization in Agent Studio Mode

Recognizing a connection between the new content and another domain, the user customizes the agent's logic.

1. User Action: The user switches to the Agent Studio Mode to access the agent's source code.
2. User Action: In the Middle Panel (The Dana Editor), the user modifies the Dana agent's code. The modification explicitly instructs the Neuroscience Agent to seek connections to concepts like "neural networks" within the CompSci Domain's knowledge base.
3. User Action: The user immediately tests the modified agent logic in the Bottom Panel (The REPL & Logs) using a command such as dana> agent.query("test context"), confirming the new cross-domain search capability.

Step 4: Complex Query via Global Orchestrator Chat

Finally, the user leverages the system's multi-agent architecture to synthesize knowledge across domains.

1. User Action: The user navigates to the Global Orchestrator Chat.
2. User Action: In the Domain Scope Selector, the user explicitly checks the boxes for both "Neuroscience" and "CompSci".
3. User Action: The user inputs a complex, cross-domain query: "How does the lecture I just watched relate to current LLM architecture?"
4. System Action: The Orchestrator Agent intelligently calls both the newly customized Neuroscience Agent and the CompSci Agent, synthesizes their respective findings, and delivers a comprehensive, integrated answer to the user.