Multi-Agent based Automation of Stress Testing Under Custom Scenarios

This primary agent ingests a risk manager’s natural language or structured input (e.g., 'simulate a 30% oil spike combined with Eastern European currency devaluation') and orchestrates the data retrieval. It calls internal portfolio systems for live holdings and connects to external APIs for real-time risk factor data (volatility surfaces, commodity curves, credit spreads). It validates data completeness and triggers the simulation engine, ensuring each custom scenario has a clean, timestamped input dataset.

A high-performance compute agent executes the prescribed shock logic against the portfolio’s risk factor mappings. It leverages cloud-based containers or HPC clusters to run thousands of revaluations using pre-configured pricing models (e.g., Black-Scholes for options, duration/convexity for bonds). This agent generates the raw P&L and risk metric deltas (Delta-VaR, Expected Shortfall) under the stress scenario, outputting a detailed dataset for analysis. It is designed for parallel processing to deliver results in minutes, not hours.

This agent transforms raw numerical outputs into actionable intelligence. It identifies the top 5-10 drivers of loss (e.g., 'Energy sector equities contributed 60% of the total $2.4M simulated loss'), flags any breaches of internal stress limits, and compares the results to baseline regulatory stresses. Using RAG over historical commentary and compliance guidelines, it drafts a concise summary report, highlighting key vulnerabilities and recommended follow-up actions for the risk team.

A governance agent enforces control planes. If a simulated loss exceeds a pre-defined materiality threshold, it automatically pauses the workflow and creates a ticket in the team’s incident management system (e.g., Jira, ServiceNow), tagging the responsible portfolio manager and chief risk officer. It manages the approval chain for releasing the final report, logs all human overrides or acknowledgments, and ensures a complete audit trail from scenario idea to sanctioned result.

Operating continuously in the background, this agent monitors the health of the entire stress-testing pipeline. It tracks data feed latency, model calibration drift, and compute resource utilization. It alerts DevOps or the quant team if input data quality degrades or if simulation runtimes exceed SLA. This agent also periodically re-runs a suite of baseline scenarios to ensure result consistency over time, providing essential operational integrity for a production risk system.

This agent handles the bidirectional sync with enterprise systems. It writes finalized, approved stress test results and their metadata back to the risk data warehouse (e.g., Snowflake, Databricks) and updates dashboards in Tableau or Power BI. It can also trigger downstream actions, such as creating a hedging analysis ticket in a portfolio management system (like BlackRock Aladdin or SimCorp Dimension) if a severe vulnerability is confirmed, closing the loop from analysis to potential action.

Define the custom scenario logic, risk factor models, and impact thresholds. They provide the domain expertise on what constitutes a plausible stress event (e.g., a 50% spike in oil prices combined with a specific geopolitical escalation) and validate the economic plausibility of the agent-generated outputs. Their sign-off is required on scenario libraries and the interpretation logic before production deployment.

Design the multi-agent orchestration using frameworks like LangGraph or CrewAI. They architect the data flows from market data providers (Bloomberg, Refinitiv) and internal portfolio systems, define the agent roles (Scenario Parser, Factor Modeler, Impact Calculator, Report Generator), and implement the approval gates for human review. This role ensures the system is scalable, observable, and integrates with existing risk platforms.

Act as the primary consumers of the stress test outputs. They define the actionable thresholds (e.g., "alert if portfolio VaR impact exceeds $X million") and participate in the design of the escalation workflow. Their feedback during the pilot phase is critical to ensure the automated reports trigger timely hedging or rotation decisions, moving beyond regulatory box-ticking to active risk management.

Build and maintain the pipelines that feed live positions, security master data, and historical risk factor time series into the workflow. They containerize the agentic application, manage its deployment on cloud or on-prem infrastructure, and implement the monitoring and logging (e.g., using Prometheus/Grafana) to track agent performance, data freshness, and compute costs for thousands of concurrent simulations.

Govern the entire workflow. They audit the scenario definition process for bias or oversimplification, validate the statistical models used by the agents, and ensure the audit trail captures all inputs, agent decisions, and outputs for regulatory scrutiny (e.g., SR 11-7, Basel). Their approval is a non-negotiable gate before any automated output can influence trading decisions.

Provides the architectural blueprint, accelerator code for agent orchestration, and integration patterns for core systems like MSCI RiskManager, BlackRock Aladdin, or proprietary risk engines. We manage the project delivery, facilitate stakeholder workshops to define agent behaviors, and hand over a production-ready, documented system with defined SLAs for ongoing support and iteration.