Multi-Agent Orchestration
Description
SBA maintains millions of lines of legacy ColdFusion and COBOL code supporting mission-critical lending systems (CAFS) with minimal test coverage and significant technical debt. Manual code refactoring, test case generation, and project management across modernization initiatives are resource-intensive and error-prone, creating risk during cloud migration.
Detailed example
Multi-agent system using Amazon Bedrock with foundation models. Agent Teams coordinate specialized agents for: (1) Code Refactoring — analyzes legacy ColdFusion/COBOL code and recommends modernization patterns, (2) Automated Testing — generates unit, integration, and regression test cases for financial calculation modules, (3) Project Management — synthesizes sprint status, identifies blockers, and coordinates task dependencies across modernization teams. Input: source code repositories, project management data, test specifications. Output: refactored code suggestions, test case files, project status summaries, risk assessments. All outputs require human review and approval before implementation.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Multi-agent AI orchestration will accelerate legacy code modernization by automating code refactoring recommendations, generating comprehensive test suites for financial calculation modules, and streamlining project management workflows. Expected benefits include reduced manual effort for QA testing, improved code quality and test coverage for CAFS applications, faster time-to-migration for AWS EVS initiatives, and better coordination across modernization workstreams.
Audit / financial statement impact
Not high impact: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21.
Controls / human review
ATO: Yes; PIA: Not publicly available
Data needed
No model training or fine-tuning is performed. Uses foundation models via Amazon Bedrock inference API with agency-provided context through retrieval-augmented generation (RAG) and prompt engineering. Code repositories and project data are provided at inference time only.