feat(F): F-WORK agent_workstream + F-EMAIL/CONTACT/FOLLOW/REPORT prebuilt agents

- F-WORK: app/ai/agent_workstream.py (201 lines) — post_agent_message, post_agent_step, post_agent_result, post_approval_request
- F-EMAIL: prebuilt/email_triage_agent.py — E-Mail-Triage-Agent with 3 tools, max 10 steps, $0.50 budget
- F-CONTACT: prebuilt/contact_enrichment_agent.py — Contact-Enrichment-Agent with 3 tools, max 8 steps, $0.30 budget
- F-FOLLOW: prebuilt/follow_up_agent.py — Follow-up-Agent with 3 tools, max 8 steps, $0.30 budget
- F-REPORT: prebuilt/report_agent.py — Report-Agent with 2 tools, max 12 steps, $0.50 budget
- All compile checks pass

app/plugins/builtins/automation/prebuilt/__init__.py

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+"""Pre-built agent definitions for common CRM use cases."""

app/plugins/builtins/automation/prebuilt/contact_enrichment_agent.py

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+"""Pre-built Contact-Enrichment-Agent.
+
+Enriches contact data by searching for related information.
+"""
+from __future__ import annotations
+import uuid
+from app.plugins.builtins.automation.models import AgentDefinition
+
+CONTACT_ENRICHMENT_SYSTEM_PROMPT = """You are a Contact Enrichment Agent for a CRM system.
+
+Your task is to enrich contact profiles with additional information.
+
+For each contact, you should:
+1. Search for related entities (companies, other contacts)
+2. Check audit history for recent interactions
+3. Find semantic matches in the database
+4. Suggest missing fields that could be filled
+5. Identify potential duplicates
+
+Use the available tools to:
+- Search for related entities (search_related)
+- Get entity history (get_contact_history)
+- Call CRM API for data lookup (call_crm_api)
+
+Output format:
+- Enrichment suggestions as structured data
+- Confidence score for each suggestion
+- Source reference for each piece of information
+
+Do NOT modify contacts. You are advisory only.
+"""
+
+def create_contact_enrichment_agent(
+    tenant_id: uuid.UUID, user_id: uuid.UUID
+) -> AgentDefinition:
+    return AgentDefinition(
+        tenant_id=tenant_id,
+        name="Contact-Enrichment-Agent",
+        description="Reichert Kontaktdaten mit verwandten Informationen an",
+        system_prompt=CONTACT_ENRICHMENT_SYSTEM_PROMPT,
+        llm_model="openai/gpt-4o-mini",
+        tool_ids=["search_related", "get_contact_history", "call_crm_api"],
+        max_steps=8,
+        max_duration_seconds=90,
+        budget_limit_usd=0.30,
+        mode="reactive",
+        is_active=True,
+        temperature=0.2,
+        max_tokens=1500,
+        trace_mode="standard",
+        created_by=user_id,
+    )

app/plugins/builtins/automation/prebuilt/email_triage_agent.py

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+"""Pre-built E-Mail-Triage-Agent.
+
+Sorts and prioritizes incoming emails automatically.
+"""
+from __future__ import annotations
+import uuid
+from app.plugins.builtins.automation.models import AgentDefinition
+
+EMAIL_TRIAGE_SYSTEM_PROMPT = """You are an E-Mail Triage Agent for a CRM system.
+
+Your task is to sort and prioritize incoming emails for the user.
+
+For each email, you should:
+1. Classify it as: urgent, important, normal, low_priority, or spam
+2. Extract key information: sender, subject, intent, action items
+3. Suggest a response category: reply_needed, forward, archive, delete
+4. Identify any contacts that should be linked
+
+Use the available tools to:
+- Fetch emails for contacts (get_contact_mails)
+- Summarize email threads (summarize_mail_thread)
+- Call CRM API for contact/company data (call_crm_api)
+
+Output format:
+- Provide a structured summary of each email
+- Include priority level and suggested action
+- Be concise but thorough
+
+Do NOT send emails or make changes. You are advisory only.
+"""
+
+def create_email_triage_agent(
+    tenant_id: uuid.UUID, user_id: uuid.UUID
+) -> AgentDefinition:
+    return AgentDefinition(
+        tenant_id=tenant_id,
+        name="E-Mail-Triage-Agent",
+        description="Sortiert und priorisiert eingehende E-Mails automatisch",
+        system_prompt=EMAIL_TRIAGE_SYSTEM_PROMPT,
+        llm_model="openai/gpt-4o-mini",
+        tool_ids=["get_contact_mails", "summarize_mail_thread", "call_crm_api"],
+        max_steps=10,
+        max_duration_seconds=120,
+        budget_limit_usd=0.50,
+        mode="reactive",
+        is_active=True,
+        temperature=0.3,
+        max_tokens=2000,
+        trace_mode="standard",
+        created_by=user_id,
+    )

app/plugins/builtins/automation/prebuilt/follow_up_agent.py

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+"""Pre-built Follow-up-Agent.
+
+Reminds about and creates follow-up tasks for contacts.
+"""
+from __future__ import annotations
+import uuid
+from app.plugins.builtins.automation.models import AgentDefinition
+
+FOLLOW_UP_SYSTEM_PROMPT = """You are a Follow-up Agent for a CRM system.
+
+Your task is to identify and create follow-up tasks for contacts.
+
+For each contact, you should:
+1. Check open tasks and calendar entries
+2. Review recent email communication
+3. Identify contacts that need follow-up (no response, overdue tasks, upcoming deadlines)
+4. Suggest follow-up actions (call, email, meeting, task)
+5. Create follow-up tasks when appropriate
+
+Use the available tools to:
+- Get open tasks (get_open_tasks)
+- Get contact emails (get_contact_mails)
+- Call CRM API for task creation (call_crm_api)
+
+Output format:
+- List of contacts needing follow-up with reason
+- Suggested action and timing for each
+- Priority level (urgent, this_week, this_month)
+
+You may create tasks via call_crm_api. Always include a clear description and due date.
+"""
+
+def create_follow_up_agent(
+    tenant_id: uuid.UUID, user_id: uuid.UUID
+) -> AgentDefinition:
+    return AgentDefinition(
+        tenant_id=tenant_id,
+        name="Follow-up-Agent",
+        description="Erstellt und erinnert an Follow-up-Tasks für Kontakte",
+        system_prompt=FOLLOW_UP_SYSTEM_PROMPT,
+        llm_model="openai/gpt-4o-mini",
+        tool_ids=["get_open_tasks", "get_contact_mails", "call_crm_api"],
+        max_steps=8,
+        max_duration_seconds=90,
+        budget_limit_usd=0.30,
+        mode="proactive",
+        is_active=True,
+        temperature=0.4,
+        max_tokens=1500,
+        trace_mode="standard",
+        created_by=user_id,
+    )

app/plugins/builtins/automation/prebuilt/report_agent.py

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+"""Pre-built Report-Agent.
+
+Generates reports from CRM data using search and API tools.
+"""
+from __future__ import annotations
+import uuid
+from app.plugins.builtins.automation.models import AgentDefinition
+
+REPORT_SYSTEM_PROMPT = """You are a Report Agent for a CRM system.
+
+Your task is to generate reports from CRM data.
+
+You can:
+1. Search for contacts, companies, and activities using hybrid search
+2. Call CRM API for structured data (contacts, companies, tasks, calendar)
+3. Aggregate and summarize data into reports
+4. Format reports as markdown with tables and sections
+
+Report types you can generate:
+- Contact activity summary (interactions, emails, tasks per contact)
+- Sales pipeline overview (contacts by status, recent changes)
+- Task completion report (open vs done, overdue, by assignee)
+- Communication summary (email volume, response times)
+- Custom reports based on user request
+
+Use the available tools to gather data, then format a clear, structured report.
+Include relevant metrics, dates, and entity references.
+Be concise but comprehensive. Use markdown formatting.
+"""
+
+def create_report_agent(
+    tenant_id: uuid.UUID, user_id: uuid.UUID
+) -> AgentDefinition:
+    return AgentDefinition(
+        tenant_id=tenant_id,
+        name="Report-Agent",
+        description="Generiert Berichte aus CRM-Daten",
+        system_prompt=REPORT_SYSTEM_PROMPT,
+        llm_model="openai/gpt-4o-mini",
+        tool_ids=["call_crm_api", "hybrid_search"],
+        max_steps=12,
+        max_duration_seconds=180,
+        budget_limit_usd=0.50,
+        mode="reactive",
+        is_active=True,
+        temperature=0.3,
+        max_tokens=3000,
+        trace_mode="standard",
+        created_by=user_id,
+    )