LangGraph Applications in Finance Training Course
LangGraph serves as a framework designed for constructing stateful, multi-agent Large Language Model (LLM) applications through composable graphs, featuring persistent state and precise execution control.
This instructor-led, live training—available either online or onsite—is tailored for intermediate to advanced professionals seeking to design, implement, and manage LangGraph-based financial solutions that adhere to strict governance, observability, and compliance standards.
Upon completion of this training, participants will be equipped to:
- Design finance-specific LangGraph workflows that align with regulatory and audit requirements.
- Integrate financial data standards and ontologies into graph states and associated tooling.
- Implement reliability, safety, and human-in-the-loop controls essential for critical processes.
- Deploy, monitor, and optimize LangGraph systems to ensure optimal performance, cost-efficiency, and adherence to Service Level Agreements (SLAs).
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical applications.
- Hands-on implementation within a live laboratory environment.
Customization Options
- To request customized training for this course, please contact us to arrange your specific needs.
Course Outline
LangGraph Fundamentals for Finance
- Review of LangGraph architecture and stateful execution mechanisms.
- Financial use cases, including research copilots, trade support, and customer service agents.
- Considerations for regulatory constraints and auditability.
Financial Data Standards and Ontologies
- Basics of ISO 20022, FpML, and FIX standards.
- Mapping schemas and ontologies into graph state.
- Managing data quality, lineage, and Personally Identifiable Information (PII).
Workflow Orchestration for Financial Processes
- Workflows for KYC (Know Your Customer) and AML (Anti-Money Laundering) onboarding.
- Trade lifecycle management, handling exceptions, and case management.
- Credit adjudication and decision-making paths.
Compliance, Risk, and Controls
- Policy enforcement and model risk management.
- Implementing guardrails, approval processes, and human-in-the-loop steps.
- Managing audit trails, data retention, and explainability.
Integration and Deployment
- Connecting to core systems, data lakes, and APIs.
- Containerization, secret management, and environment configuration.
- CI/CD pipelines, staged rollouts, and canary deployments.
Observability and Performance
- Monitoring structured logs, metrics, traces, and costs.
- Conducting load testing, defining SLOs, and managing error budgets.
- Incident response, rollback strategies, and resilience patterns.
Quality, Evaluation, and Safety
- Utilizing unit tests, scenario testing, and automated evaluation harnesses.
- Red teaming, adversarial prompt testing, and safety checks.
- Dataset curation, drift monitoring, and continuous improvement strategies.
Summary and Next Steps
Requirements
- A solid understanding of Python and LLM application development.
- Experience working with APIs, containers, or cloud services.
- Basic familiarity with financial domains or data models.
Target Audience
- Domain technologists.
- Solution architects.
- Consultants developing LLM agents for regulated industries.
Open Training Courses require 5+ participants.
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