Risk identification from unstructured data
Specialized agents identify and decompose risks found in unstructured material.
Multi-agent enterprise risk management
A Big4 professional-services firm was developing an AI system for its own risk teams and external clients. We helped build the multi-agent architecture behind it, including risk identification, decomposition, quantitative simulation, control-gap analysis and preventive treatment planning. The system combines more than a dozen specialized LangGraph agents with 20+ backend APIs to run complex risk workflows with limited manual intervention.
Client
- Sector
- Professional services, Big4
- Size
- Large enterprise
- Situation
- One enterprise risk system had to support internal risk teams and external clients with different business contexts.
Problem
Enterprise risk work combines several dependent tasks. The system had to identify risks in unstructured material, break them into assessable components, examine existing controls, estimate exposure and propose preventive treatments. Multiple specialized agents had to coordinate those tasks reliably across different data formats and risk scenarios. The architecture also had to serve the firm's own risk managers and external clients across different business contexts.
What was built
Coordinated risk analysis
More than a dozen LangGraph agents coordinate risk identification, control-gap analysis, feasibility assessment and treatment planning.
Quantifying risk and testing responses
Text-to-Monte Carlo simulation supports quantitative assessment, while other agents propose preventive treatments and test whether they are feasible.
Built for internal and external use
20+ backend APIs support the firm's internal risk teams and the external clients using the same system.
Result
- Complex risk workflows can now run almost autonomously, including risk decomposition, mitigation-feasibility assessment and control-gap analysis
- More than a dozen specialized LangGraph agents
- 20+ backend APIs
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