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Duration 35 hours
Course Outline
LangGraph Fundamentals for Finance
- Review of LangGraph architecture and stateful execution models.
- Exploration of financial use cases, including research copilots, trade support, and customer service agents.
- Discussion of regulatory constraints and auditability requirements.
Financial Data Standards and Ontologies
- Overview of ISO 20022, FpML, and FIX standards.
- Mapping schemas and ontologies to graph state.
- Addressing data quality, lineage, and PII handling.
Workflow Orchestration for Financial Processes
- Designing KYC and AML onboarding workflows.
- Managing the trade lifecycle, exceptions, and case management.
- Configuring credit adjudication and decisioning paths.
Compliance, Risk, and Controls
- Implementing policy enforcement and model risk management.
- Establishing guardrails, approval mechanisms, and human-in-the-loop steps.
- Maintaining audit trails, data retention, and explainability.
Integration and Deployment
- Connecting to core systems, data lakes, and external APIs.
- Handling containerization, secrets management, and environment configuration.
- Setting up CI/CD pipelines, staged rollouts, and canary releases.
Observability and Performance
- Utilizing structured logs, metrics, traces, and cost monitoring.
- Conducting load testing, defining SLOs, and managing error budgets.
- Establishing incident response, rollback, and resilience patterns.
Quality, Evaluation, and Safety
- Building unit, scenario, and automated evaluation harnesses.
- Performing red teaming, adversarial prompt testing, and safety checks.
- Curating datasets, monitoring drift, and driving continuous improvement.
Summary and Next Steps
Requirements
- Solid understanding of Python and LLM application development.
- Experience working with APIs, containers, or cloud services.
- Familiarity with financial domains or data models.
Audience
- Domain technologists.
- Solution architects.
- Consultants developing LLM agents within regulated industries.