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Duration 35 hours
Course Outline
Core LangGraph Concepts for Financial Applications
- A review of LangGraph’s architecture and its stateful execution capabilities.
- Exploration of financial use cases, including research copilots, trade support systems, and customer service agents.
- Addressing regulatory constraints and ensuring auditability.
Adopting Financial Data Standards and Ontologies
- Foundational understanding of ISO 20022, FpML, and FIX protocols.
- Strategies for mapping schemas and ontologies within the graph state.
- Managing data quality, lineage tracking, and Personally Identifiable Information (PII) protection.
Orchestrating Workflows for Financial Operations
- Designing KYC and AML onboarding processes.
- Managing the trade lifecycle, handling exceptions, and overseeing case management.
- Structuring credit adjudication and decision-making paths.
Ensuring Compliance, Risk Management, and Control
- Implementing policy enforcement and model risk management practices.
- Instituting guardrails, approval workflows, and human-in-the-loop interventions.
- Maintaining comprehensive audit trails, data retention, and model explainability.
System Integration and Deployment Strategies
- Establishing connections with core banking systems, data lakes, and external APIs.
- Managing containerization, secret management, and environment configurations.
- Utilizing CI/CD pipelines, staged rollouts, and canary releases.
Enhancing Observability and Performance
- Implementing structured logging, metrics collection, tracing, and cost monitoring.
- Conducting load testing, defining SLOs, and managing error budgets.
- Developing incident response strategies, rollback procedures, and resilience patterns.
Quality Assurance, Evaluation, and Safety
- Building unit tests, scenario tests, and automated evaluation harnesses.
- Performing red teaming exercises, handling adversarial prompts, and verifying safety checks.
- Curating datasets, monitoring drift, and driving continuous improvement.
Conclusion and Future Directions
Requirements
- Solid proficiency in Python and the development of LLM-based applications.
- Practical experience interacting with APIs, containerization technologies, or cloud service platforms.
- Fundamental knowledge of financial industry domains or data modeling concepts.
Target Audience
- Domain-focused technologists.
- Solution architects.
- Consultants specializing in building LLM agents within heavily regulated industries.
