version: v0.3.0 # Define the Risk Crew Agent service agents: - id: risk_crew_agent #url: http://localhost:10530/v1/chat/completions url: http://host.docker.internal:10530/v1/chat/completions # HTTP filter for PII redaction and prompt injection detection filters: - id: pii_security_filter #url: http://localhost:10550/v1/tools/pii_security_filter url: http://host.docker.internal:10550/v1/tools/pii_security_filter type: http # LLM providers with model routing model_providers: - model: openai/gpt-4o access_key: $OPENAI_API_KEY default: true - model: openai/gpt-4o-mini access_key: $OPENAI_API_KEY # ToDo: Debug model aliases # Model aliases for semantic naming model_aliases: risk_fast: target: openai/gpt-4o-mini risk_reasoning: target: openai/gpt-4o # Listeners listeners: # Agent listener for routing credit risk requests - type: agent name: credit_risk_service port: 8001 router: plano_orchestrator_v1 address: 0.0.0.0 agents: - id: risk_crew_agent description: | Credit Risk Case Copilot Agent - Specialized AI system for credit risk assessment, policy compliance, and case management. CAPABILITIES: * Credit risk triage and assessment for loan applications * Multi-agent workflow using intake, scoring, policy, and memo agents * Risk band classification (LOW/MEDIUM/HIGH) with confidence scoring * Risk driver identification with supporting evidence from application data * Policy and compliance checks against lending standards * Document requirement identification based on risk profile * Bank-ready decision memo generation * Case creation with structured data capture * Handles missing data, thin files, and incomplete applications USE CASES: * "Analyze this loan application for credit risk" * "What is the risk assessment for this applicant?" * "Check policy compliance for this case" * "Create a decision memo for this application" * "What documents are needed for this loan?" * "Assess the credit risk and create a case" SECURITY & COMPLIANCE: * PII redaction for CNIC, phone numbers, emails * Prompt injection detection and mitigation * Audit trail with model usage and guardrail events * OpenTelemetry tracing for compliance monitoring When queries involve credit risk assessment, policy validation, document requirements, decision memos, or case creation for loan applications, route to this agent. filter_chain: - pii_security_filter # Model listener for internal LLM gateway (used by agents) - type: model name: llm_gateway address: 0.0.0.0 port: 12000 # Prompt listener for function calling - type: prompt name: prompt_functions address: 0.0.0.0 port: 10000 # Endpoints for prompt targets endpoints: case_service: #endpoint: localhost:10540 endpoint: host.docker.internal:10540 connect_timeout: 5s # Prompt target for case creation prompt_targets: - name: create_risk_case description: Create a new credit risk case in the case management system with validated loan application data parameters: - name: applicant_name description: Full name of the loan applicant required: true type: string - name: loan_amount description: Requested loan amount in currency required: true type: number - name: risk_band description: Risk classification (LOW, MEDIUM, or HIGH) required: true type: string enum: ["LOW", "MEDIUM", "HIGH"] - name: confidence description: Confidence score for risk assessment (0.0 to 1.0) required: true type: number - name: recommended_action description: Recommended action (APPROVE, CONDITIONAL_APPROVE, REFER, REJECT) required: true type: string enum: ["APPROVE", "CONDITIONAL_APPROVE", "REFER", "REJECT"] - name: required_documents description: List of required documents for case processing required: true type: array - name: policy_exceptions description: List of policy exceptions identified required: false type: array - name: notes description: Additional case notes or decision memo summary required: false type: string endpoint: name: case_service path: /cases http_method: POST # OpenTelemetry tracing tracing: random_sampling: 100