# Implementation Summary: Modular Agentic Framework

## Overview
We have successfully implemented the core Agentic Framework for the PHQ-8 assessment benchmark. This architecture uses **LangGraph** to orchestrate a multi-stage reasoning process ("RISEN") and supports dynamic switching between Local (Tier 1) and Cloud (Tier 3) models. The system is fully integrated with the MongoDB infrastructure for data and prompts.

## Completed Components

### 1. Agent Architecture (`src/helia/agent/graph.py`)
- Implemented a `StateGraph` that manages the workflow lifecycle.
- **Nodes**: `ingestion`, `extract_evidence`, `map_criteria`, `score_item`, `human_review`, `persistence`.
- **Routing**: Conditional edges loop through the 8 PHQ-8 items before proceeding to review.
- **HITL**: Configured `MemorySaver` to allow human-in-the-loop interrupts at the `human_review` stage.

### 2. State Management (`src/helia/agent/state.py`)
- Created `ClinicalState` Pydantic model.
- Strictly types the workflow memory, including:
  - `transcript_text`: The input data.
  - `scores`: A list of `PHQ8ItemScore` objects (accumulated via reducer).
  - `current_item_index`: Tracks progress through the 8 items.
  - `current_evidence` / `current_reasoning`: Transient fields for the RISEN loop.

### 3. RISEN Logic (`src/helia/agent/nodes/assessment.py`)
- Refactored the monolithic evaluation logic into three granular nodes:
  1.  **Extract**: Finds verbatim quotes for the specific symptom.
  2.  **Map**: Aligns evidence to the 0-3 scoring criteria.
  3.  **Score**: Assigns the final value and structured reasoning.
- **Prompt Management**: Fetches prompts dynamically from the MongoDB `Prompt` collection using `Prompt.find_one`.

### 4. Runner & Config (`src/helia/agent/runner.py`)
- Created a CLI entry point: `python -m helia.agent.runner <run_id>`.
- Initializes the MongoDB connection via `init_db`.
- Fetches all available `Transcript` documents from the database to run the benchmark.
- Injects the specific `RunConfig` (Tier 1/2/3) into the graph's runtime configuration.

### 5. Ingestion (`src/helia/ingestion/loader.py`)
- Added `ClinicalDataLoader` to abstract transcript fetching.
- Loads directly from the `Transcript` Beanie document model in MongoDB.

### 6. Database Migrations
- Created `migrations/init_risen_prompts.py` to seed the database with the required "RISEN" prompt templates (`phq8-extract`, `phq8-map`, `phq8-score`).

## Usage

1. **Seed Prompts**:
   ```bash
   python migrations/init_risen_prompts.py
   ```

2. **Run Agent**:
   ```bash
   # Run with the default Tier 3 (Cloud) config (defined in config.yaml)
   python -m helia.agent.runner gemini-flash
   ```

## Next Steps
1.  **Safety**: Implement the `Safety Guardrail` (parallel node) as designed in `plans/safety-guardrail-architecture.md`.
2.  **Persistence**: Uncomment the DB save logic in `persistence_node` to save `AssessmentResult` documents.