SFCR Agentic Workflow
Deploys a **multi-agent AI orchestration system** with six specialized agents that autonomously collect financial data, validate regulatory compliance, generate report narratives, perform quality assurance, and produce XBRL outputs. Human-in-the-loop checkpoints ensure accuracy while significantly reducing report generation time.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
AI Agents
Specialized autonomous agents working in coordination
Orchestrator Agent
Complex SFCR generation requires **coordinating multiple specialized tasks** across data collection, compliance checking, narrative generation, and formatting—with dependencies that must be managed to ensure correct sequencing.
Core Logic
Acts as the **master coordinator** that delegates tasks to specialized agents, manages workflow state transitions, handles inter-agent communication, monitors progress across all phases, and triggers escalations when agents encounter blockers or low-confidence scenarios. Uses a sitemap-based task graph to ensure proper execution order.
Data Collector Agent
SFCR reports require **consolidating data from disparate sources**—financial databases, policy management systems, risk registers, and actuarial models—which is time-consuming and error-prone when done manually.
Core Logic
Autonomously queries connected data sources using specialized tools (`query_financial_database`, `query_policy_database`, `get_risk_assessments`). Validates data completeness, applies transformation rules, and produces structured datasets with data lineage tracking for downstream agents.
Compliance Analyzer Agent
Solvency II mandates strict adherence to **Pillar 1 quantitative requirements** (SCR, MCR, technical provisions) and EIOPA guidelines. Manual compliance verification is resource-intensive and risks regulatory penalties for non-compliance.
Core Logic
Validates all financial calculations against Solvency II requirements using `validate_solvency_calculation` and `check_regulatory_compliance` tools. Cross-references EIOPA guidelines, identifies compliance gaps, generates validation reports with specific references to regulatory articles, and flags items requiring human review.
Report Generator Agent
SFCR narrative sections require **professional financial writing** that accurately represents complex quantitative data, explains risk management strategies, and maintains consistency with regulatory terminology across hundreds of pages.
Core Logic
Generates report sections using **RAG-augmented content creation** with `search_regulatory_knowledge` tool. Retrieves relevant templates, precedent language, and regulatory guidance to produce narratives. Each section includes quality scoring for readability, accuracy, and compliance alignment.
Quality Reviewer Agent
Report quality assurance requires **cross-validating multiple sections** for internal consistency, numerical accuracy, and completeness—a tedious process when performed manually across lengthy documents.
Core Logic
Performs automated validation using `cross_validate_sections` and `calculate_quality_score` tools. Checks for numerical consistency between tables and narratives, identifies gaps in required disclosures, validates terminology usage, and produces quality metrics with specific improvement recommendations.
XBRL Specialist Agent
Regulatory submission requires **XBRL-formatted instance documents** adhering to EIOPA taxonomies. Manual XBRL tagging is highly technical and error-prone, with validation failures causing submission rejections.
Core Logic
Transforms validated report data into XBRL format using `generate_xbrl_tags` and `validate_xbrl_instance` tools. Maps financial facts to correct taxonomy elements, generates instance documents, runs EIOPA validation checks, and produces submission-ready files with comprehensive error logs.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
5 technologies
Architecture Diagram
System flow visualization