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step 6 complete: four specialized agents, all registered and tested

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Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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roberto
2026-03-02 13:18:53 +01:00
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BACKEND_PLAN.md
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@@ -195,27 +195,27 @@ adiuva-api/
- Playbooks are pre-built plans for common operations (e.g., "create task from email", "generate weekly report")
- **Outcome:** Plans are cacheable as playbooks. Prompt IP never leaves the server.
### Step 6 — Chat Agents
- [ ] `app/agents/task_agent.py` — `@registry.register`:
### Step 6 — Chat Agents ✅
- [x] `app/agents/task_agent.py` — `@registry.register`:
- Description: "Manages tasks: create, update, list, suggest"
- Tools: `create_task(title, description, priority, due_date)`, `update_task(id, updates)`, `list_tasks(filters)`, `suggest_tasks(notes_context)`
- System prompt: PM-oriented, validates task structure, infers priority from context
- `handle()`: LLM + tool loop via `_tool_loop()`, returns response text + list of actions performed
- Accepts flexible context: mandatory fields `user_profile` + `message`, all other fields (from batch/plugin output) are optional
- [ ] `app/agents/calendar_agent.py` — `@registry.register`:
- [x] `app/agents/calendar_agent.py` — `@registry.register`:
- Description: "Calendar management: events, conflicts, scheduling"
- Tools: `list_events(date_range)`, `detect_conflicts(events)`, `suggest_reschedule(conflict)`
- Works with event metadata passed in context (never raw calendar data stored)
- [ ] `app/agents/email_agent.py` — `@registry.register`:
- [x] `app/agents/email_agent.py` — `@registry.register`:
- Description: "Email analysis: classify, extract actions, draft responses"
- Tools: `classify_email(metadata)`, `extract_action_items(metadata)`, `draft_response(thread_context)`
- Only processes metadata sent by client — never raw email bodies
- [ ] `app/agents/analytics_agent.py` — `@registry.register`:
- [x] `app/agents/analytics_agent.py` — `@registry.register`:
- Description: "Workspace analytics: metrics, reports, trends"
- Tools: `calculate_metrics(task_data)`, `generate_report(period, data)`, `trend_analysis(data_points)`
- Crunches numbers from context, returns structured insights
- [ ] `app/agents/__init__.py`: imports all agent modules to trigger `@registry.register` decorators
- [ ] Unit tests per agent with mocked LLM
- [x] `app/agents/__init__.py`: imports all agent modules to trigger `@registry.register` decorators
- [x] Unit tests per agent with mocked LLM
- **Outcome:** Four specialized agents, all registered and tested.
### Step 7 — Storage Layer