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Duration 35 hours
Course Outline
LangGraph Fundamentals for Finance
- Review of LangGraph architecture and stateful execution concepts.
- Financial use cases: research copilots, trade support, and customer service agents.
- Considerations regarding regulatory constraints and auditability.
Financial Data Standards and Ontologies
- Introduction to ISO 20022, FpML, and FIX.
- Mapping schemas and ontologies to graph states.
- Data quality, lineage, and handling of PII.
Workflow Orchestration for Financial Processes
- KYC and AML onboarding workflows.
- Trade lifecycle management, exceptions, and case handling.
- Credit adjudication and decision-making paths.
Compliance, Risk, and Controls
- Policy enforcement and model risk management.
- Guardrails, approvals, and human-in-the-loop procedures.
- Audit trails, data retention, and explainability.
Integration and Deployment
- Connecting to core systems, data lakes, and APIs.
- Containerization, secrets management, and environment configuration.
- CI/CD pipelines, staged rollouts, and canary deployments.
Observability and Performance
- Structured logging, metrics, tracing, and cost monitoring.
- Load testing, SLOs, and error budgets.
- Incident response, rollback strategies, and resilience patterns.
Quality, Evaluation, and Safety
- Unit, scenario, and automated evaluation harnesses.
- Red teaming, adversarial prompting, and safety checks.
- Dataset curation, drift monitoring, and continuous improvement.
Summary and Next Steps
Requirements
- Proficiency in Python and LLM application development
- Experience working with APIs, containers, or cloud services
- Basic knowledge of financial domains or data models
Audience
- Domain technologists
- Solution architects
- Consultants developing LLM agents for regulated industries
