step 3.5 complete: chatbot journey endpoint

AI_REFACTOR_PLAN.md

@@ -248,6 +248,8 @@ Tools must use **camelCase** field names (Drizzle maps them to snake_case intern
 > **Objective:** Backend manages all agent configuration, scheduling, orchestration, and cloud data fetching. Two agent types: **Local Directory Agent** (backend triggers Electron to read files, then AI analyzes) and **Cloud Connector Agent** (backend fetches Gmail/Teams data directly, AI analyzes, pushes results to Electron via WS tool_call). All extracted items use existing WS tool infrastructure to insert into Electron's local DB with `is_ai_suggested=True`.
 >
 > **Electron Phase 3 plan:** `../adiuva/AI_REFACTOR_PLAN.md` Phase 3 section.
+>
+> **Electron UI status (2025):** Steps 3.6, 3.7, 3.8 of the Electron plan are ✅ complete. Agents are configured inside the Settings page (`/settings?section=agents`) — not a standalone route. The `JourneyDialog` (Step 3.8) is embedded inline in the Settings → Agents section. `LocalAgentConfigPanel` and `CloudAgentConfigPanel` (Step 3.7) are also inline. This affects the journey API contract (see Step 3.5 below).
 
 ### Architecture
 
@@ -412,22 +414,27 @@ Cloud Agent:
 - **Outcome:** Backend drives all agent execution — both local (via WS file request) and cloud (direct API calls — stub until Step 3.6).
 
 ### Step 3.5 — Chatbot Journey endpoint
-- [ ] Create `app/api/routes/agent_setup.py`:
+- [x] Create `app/api/routes/agent_setup.py`:
   - `POST /api/v1/agents/journey/start`:
-    - Body: `{ agent_type: "local"|"cloud", data_types: ["tasks", "notes", ...] }`
+    - Body: `{ agent_type: "local"|"cloud", agent_id: str | None }`
+      - `agent_type`: which kind of agent this journey configures.
+      - `agent_id`: optional — if provided, the session is pre-seeded with the existing agent's `prompt_template` so the user can refine it. If absent, fresh journey.
+      - **No `data_types` field** — data types are determined through the conversation itself, not sent upfront.
     - Creates a journey session (in-memory or Redis-backed)
     - Returns first AI message: contextual question based on agent type
       - Local: "What kind of files are in the directories you want to monitor? (emails, documents, logs, etc.)"
       - Cloud: "What kind of emails/messages should I look for? (client communications, invoices, meeting notes, etc.)"
     - Response: `{ session_id, message, done: false }`
+    - **Electron note:** `proxyPost` auto-converts camelCase keys to snake_case. Electron sends `{ agentType, agentId }` → backend receives `{ agent_type, agent_id }`.
   - `POST /api/v1/agents/journey/message`:
     - Body: `{ session_id, message }`
     - AI processes user's answer, asks follow-up questions (max 5 turns)
     - System prompt: "You are configuring a data extraction agent for a freelancer. Ask about file format, what data to extract (tasks, notes, checkpoints), naming conventions, priority rules, and any special mapping. After 3-5 questions, generate a detailed prompt_template."
     - When AI determines enough context: `{ session_id, message: "Here's your configuration...", done: true, prompt_template: "..." }`
     - The `prompt_template` is a structured instruction for the extraction LLM (e.g. "Extract tasks from email. Subject becomes task title. If body contains 'urgent' or 'ASAP', set priority to 'high'. Extract due dates if mentioned.")
+    - **Electron note:** `toCamelCase` converts the response → Electron reads `promptTemplate` from the final message and auto-fills the agent config panel. User clicks "Save & apply" which calls `agent.local.update` / `agent.cloud.update` tRPC mutation.
 - **Files:** `app/api/routes/agent_setup.py`, `app/main.py`
-- **Outcome:** Users configure AI prompts through guided conversation, not manual text editing.
+- **Outcome:** Users configure AI prompts through guided conversation. Journey can refine an existing config when `agent_id` is provided. ✅
 
 ### Step 3.6 — Cloud provider integrations
 - [ ] Create `app/integrations/gmail.py`: