feat(agents): add 7 specialist agents with tools and system prompts
Agents (all following 6-step contract: _plan/_gather/_reason/_act/_report): - AccountingAgent: trial balance, chart of accounts, tax summary (HIPAA-locked) - CrmAgent: pipeline summary, lead/opportunity management, won/lost analysis - SalesAgent: sales orders, quotations, revenue by rep, expired quote detection - ProjectAgent: task tracking, blocked/overdue detection, timesheet logging - ElearningAgent: course completion, low-engagement flagging, next-course suggestion - ExpensesAgent: expense sheets, pending approvals, policy violations (HIPAA-locked) - EmployeesAgent: headcount, contracts, leaves, attendance, expired contract sweep (HIPAA-locked) Tools (one file per domain): - accounting_tools.py, crm_tools.py, sales_tools.py, project_tools.py - elearning_tools.py, expenses_tools.py, employees_tools.py System prompts: each agent has a domain-specific system.txt with rules and output format All agents implement handle_peer_request() and sweep() for proactive monitoring HIPAA-locked agents (accounting, expenses, employees) enforced via LLMRouter Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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144
agent_service/agents/elearning_agent.py
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144
agent_service/agents/elearning_agent.py
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from __future__ import annotations
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import logging
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from .base_agent import BaseAgent, AgentReport, AgentDirective, SweepReport
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from ..tools.elearning_tools import ElearningTools
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logger = logging.getLogger(__name__)
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ELEARNING_TOOLS = [
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{'name': 'get_courses', 'description': 'List eLearning courses',
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'parameters': {'active': {'type': 'boolean', 'optional': True},
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'limit': {'type': 'integer', 'optional': True}}},
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{'name': 'get_course_stats', 'description': 'Get detailed stats for a course',
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'parameters': {'channel_id': {'type': 'integer'}}},
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{'name': 'get_enrolled_users', 'description': 'Get users enrolled in a course',
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'parameters': {'channel_id': {'type': 'integer'},
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'limit': {'type': 'integer', 'optional': True}}},
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{'name': 'get_slide_completion', 'description': 'Get slide completion by user',
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'parameters': {'channel_id': {'type': 'integer'},
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'min_completion': {'type': 'number', 'optional': True}}},
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{'name': 'get_learning_summary', 'description': 'Get overall learning summary', 'parameters': {}},
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{'name': 'flag_low_completion', 'description': 'Flag a course with low completion',
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'parameters': {'channel_id': {'type': 'integer'}, 'reason': {'type': 'string'}}},
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{'name': 'suggest_next_course', 'description': 'Suggest next course for a learner',
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'parameters': {'partner_id': {'type': 'integer'}}},
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{'name': 'post_chatter_note', 'description': 'Post a note on a record',
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'parameters': {'model': {'type': 'string'}, 'record_id': {'type': 'integer'},
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'note': {'type': 'string'}}},
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]
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class ElearningAgent(BaseAgent):
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name = 'elearning_agent'
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domain = 'elearning'
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required_odoo_module = 'website_slides'
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system_prompt_file = 'elearning_system.txt'
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tools = ELEARNING_TOOLS
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def __init__(self, odoo, llm, peer_bus=None):
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super().__init__(odoo, llm, peer_bus)
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self._el = ElearningTools(odoo)
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self._gathered_data = {}
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self._actions_taken = []
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self._escalations_list = []
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async def _plan(self, directive: AgentDirective) -> dict:
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intent = (directive.intent or '').lower()
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return {
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'fetch_summary': any(k in intent for k in ('summary', 'overview', 'learning')),
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'fetch_courses': 'course' in intent,
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'channel_id': directive.context.get('channel_id'),
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'partner_id': directive.context.get('partner_id'),
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}
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async def _gather(self, ctx: dict) -> dict:
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plan = ctx.get('plan', {})
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data: dict = {}
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data['summary'] = await self._el.get_learning_summary()
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if plan.get('fetch_courses') or plan.get('channel_id'):
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if plan.get('channel_id'):
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data['course_stats'] = await self._el.get_course_stats(channel_id=plan['channel_id'])
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else:
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data['courses'] = await self._el.get_courses(limit=20)
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self._gathered_data = data
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return data
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async def _reason(self, ctx: dict) -> dict:
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data = self._gathered_data
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analysis: dict = {'escalations': [], 'low_completion': []}
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summary = data.get('summary', {})
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low = summary.get('low_completion_courses', [])
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analysis['low_completion'] = low
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if len(low) > 3:
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analysis['escalations'].append(f'{len(low)} courses have <30% completion rate.')
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self._escalations_list = analysis['escalations']
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return analysis
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async def _act(self, ctx: dict) -> list:
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actions = []
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analysis = ctx.get('analysis', {})
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for course in analysis.get('low_completion', [])[:3]:
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try:
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await self._el.flag_low_completion(
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channel_id=course.get('id'),
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reason=f'Completion rate {course.get("completion_rate", 0):.1f}% is below 30% threshold',
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)
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actions.append({'action': 'flag_low_completion', 'course_id': course.get('id'), 'success': True})
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except Exception as exc:
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logger.warning('flag_low_completion failed: %s', exc)
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self._actions_taken = actions
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return actions
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async def _report(self, ctx: dict) -> AgentReport:
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data = self._gathered_data
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summary = data.get('summary', {})
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parts = []
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if summary:
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parts.append(
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f'eLearning: {summary.get("total_courses", 0)} courses, '
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f'{summary.get("total_enrollments", 0)} enrollments, '
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f'{summary.get("avg_completion", 0):.1f}% avg completion.'
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)
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if not parts:
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parts.append('eLearning review complete.')
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return AgentReport(agent=self.name, summary=chr(10).join(parts),
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data=data, escalations=self._escalations_list, actions_taken=self._actions_taken)
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async def _dispatch_tool(self, name: str, args: dict):
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dispatch = {
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'get_courses': self._el.get_courses,
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'get_course_stats': self._el.get_course_stats,
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'get_enrolled_users': self._el.get_enrolled_users,
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'get_slide_completion': self._el.get_slide_completion,
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'get_learning_summary': self._el.get_learning_summary,
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'flag_low_completion': self._el.flag_low_completion,
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'suggest_next_course': self._el.suggest_next_course,
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'post_chatter_note': self._el.post_chatter_note,
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}
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if name not in dispatch:
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raise ValueError(f'Unknown tool: {name}')
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return await dispatch[name](**args)
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async def handle_peer_request(self, request: dict) -> dict:
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req_type = request.get('type', '')
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try:
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if req_type == 'learning_summary':
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return await self._el.get_learning_summary()
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if req_type == 'suggest_courses':
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return {'courses': await self._el.suggest_next_course(partner_id=request['partner_id'])}
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return {'error': f'Unknown type: {req_type}'}
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except Exception as exc:
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return {'error': str(exc)}
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async def sweep(self) -> SweepReport:
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findings = []
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try:
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summary = await self._el.get_learning_summary()
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for course in summary.get('low_completion_courses', []):
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findings.append({'type': 'low_completion', 'course_id': course.get('id'),
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'name': course.get('name'), 'completion': course.get('completion_rate', 0),
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'severity': 'medium'})
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except Exception as exc:
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return SweepReport(agent=self.name, findings=[], actions=[], error=str(exc))
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return SweepReport(agent=self.name, findings=findings, actions=[],
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summary=f'eLearning sweep: {len(findings)} low-completion courses.')
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