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Duration 35 hours
Course Outline
LangGraph Fundamentals for Finance
- A refresher on LangGraph architecture and stateful execution.
- Financial use cases: research copilots, trade support, and customer service agents.
- Considerations for regulatory constraints and auditability.
Financial Data Standards and Ontologies
- Foundations of ISO 20022, FpML, and FIX.
- Incorporating schemas and ontologies into graph state.
- Handling data quality, lineage, and PII.
Workflow Orchestration for Financial Processes
- KYC and AML onboarding workflows.
- Managing trade lifecycle, exceptions, and cases.
- Credit adjudication and decision paths.
Compliance, Risk, and Controls
- Enforcing policies and managing model risk.
- Implementing guardrails, approvals, and human-in-the-loop steps.
- Maintaining audit trails, retention, and explainability.
Integration and Deployment
- Connecting to core systems, data lakes, and APIs.
- Managing containerization, secrets, and environments.
- Utilizing CI/CD pipelines, staged rollouts, and canary releases.
Observability and Performance
- Monitoring structured logs, metrics, traces, and costs.
- Conducting load testing and managing SLOs and error budgets.
- Establishing incident response, rollback, and resilience patterns.
Quality, Evaluation, and Safety
- Building unit, scenario, and automated evaluation harnesses.
- Performing red teaming, adversarial prompting, and safety checks.
- Curating datasets and monitoring drift for continuous improvement.
Summary and Next Steps
Requirements
- Familiarity with Python and LLM application development
- Experience with APIs, containers, or cloud services
- Basic knowledge of financial domains or data models
Audience
- Domain technologists
- Solution architects
- Consultants developing LLM agents in regulated sectors
