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Duration 35 hours
Course Outline
LangGraph Fundamentals for Finance
- A refresher on LangGraph architecture and stateful execution.
- Finance use cases, including research copilots, trade support, and customer service agents.
- Regulatory constraints and auditability considerations.
Financial Data Standards and Ontologies
- Fundamentals of ISO 20022, FpML, and FIX.
- Mapping schemas and ontologies into graph state.
- Data quality, lineage, and PII handling.
Workflow Orchestration for Financial Processes
- KYC and AML onboarding workflows.
- Trade lifecycle, exception handling, and case management.
- Credit adjudication and decisioning paths.
Compliance, Risk, and Controls
- Policy enforcement and model risk management.
- Guardrails, approvals, and human-in-the-loop steps.
- 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 logs, metrics, traces, and cost monitoring.
- Load testing, SLOs, and error budgets.
- Incident response, rollback procedures, and resilience patterns.
Quality, Evaluation, and Safety
- Unit, scenario, and automated evaluation harnesses.
- Red teaming, adversarial prompts, and safety checks.
- Dataset curation, drift monitoring, and continuous improvement.
Summary and Next Steps
Requirements
- A solid understanding of Python and LLM application development
- Experience with APIs, containers, or cloud services
- Basic familiarity with financial domains or data models
Audience
- Domain technologists
- Solution architects
- Consultants building LLM agents in regulated industries
