Transforming API Testing with Agentic AI for a Leading Financial Services Enterprise
Accelerated Quality Engineering workflow with Agentic AI-powered solution
Client
Financial Services Enterprise
Industry
Financial Services
Capital Markets
Solution
Agentic AI-Powered Quality Engineering
API Test Automation
Use Case
Enterprise Software Development
Autonomous STLC
Technology
Agentic AI
Large Language Models (LLMs)
Goal
The client wanted to modernize its API testing process as the complexity and volume of its APIs continued to grow.
The goal was to significantly increase the speed and scale of API testing while integrating seamlessly with existing workflow, while maintaining appropriate human control over validation and approval.
Problem Statement
Enterprise engineering teams spend a disproportionate share of QA effort writing and maintaining API test suites manually. As API ecosystems grow, keeping test suites aligned with evolving API contracts becomes increasingly difficult, impacting productivity and release velocity.
The client’s existing API quality engineering approach created below challenges as the number and complexity of APIs increased.
- High manual effort in creating and maintaining API test suites
- Long validation cycles for changes across multiple applications
- Inconsistent test quality and coverage across teams and applications
- Repetitive engineering work in test development and maintenance
- Limited scalability as the API landscape continued to grow
- Fragmented test artifacts and results, making traceability and regression analysis more difficult
This demands an intelligent, scalable approach to API quality engineering that combines automation with human oversight.
Solution Highlights
Vedya Labs deployed VeAssure, an in-house Agentic AI-powered quality engineering solution to automate key stages of the end-to-end testing lifecycle.
Using API specifications such as OpenAPI/Swagger, VeAssure automatically generates functional test scenarios, converts approved scenarios into executable test code, executes test suites, and produces consolidated results and coverage insights for review.
The solution was designed around three key principles:
Automation First – Automate repetitive test authoring, code generation, execution, and reporting activities.
Human-in-the-Loop Validation – Keep engineers in control through review and approval at critical checkpoints.
Scalable Execution – Support enterprise-scale testing workloads with responsive, traceable, and repeatable execution.
VeAssure enabled implementation of a comprehensive end-to-end autonomous testing workflow with:
- AI-Powered Test Case Generation – Automatically creates functional test scenarios, flows, acceptance criteria, and BDD style feature files from specifications.
- Automated Test Code Generation – Approved test cases were converted into executable test code across the required technology environments.
- Multi-Language Support – Generates tests for .NET, Java, Python, and TypeScript environments.
- Automated Code Rectification – LLM based correction mechanisms identify compilation issues in generated test code and automatically resolving
- Human Validation – Generated artifacts are reviewed and refined at a defined checkpoint before execution, allowing engineers to maintain control over AI generated testing assets.
- Automated Test Execution – Approved test code was combined with test data from CSV, JSON, and SQL sources and executed at scale and captures execution results.
- Reporting & Analytics – Produces consolidated test and coverage reports. The resulting reports captured execution outcomes, defects, trends, and coverage across code, APIs, and requirements.
- Continuous Feedback Loop – Test results and human feedback were fed back into the workflow to identify failure patterns, refine test strategies, expand coverage, and regenerate improved tests for subsequent execution.
- Artifact Management & Traceability – Maintains versioned records of generated assets and test runs.
- Enterprise Workflow Integration – The implementation connected the client’s existing engineering tools through Jira, Zephyr, GitHub, and CI/CD, allowing requirements, test cases, executable code, execution, and results to move through the established workflow
Performance Achieved
- 10- 40X Reduced Test automation cycle times
- ~95% Code Coverage Significant improvement in coverage compared
- Up to 90% Lower cost Estimated cost benchmark for manual enterprise effort versus the VeAssure workflow
Conclusion
Vedya Labs transformed the client’s API testing workflow from a largely manual process into a faster, scalable, and continuously improving testing workflow.
By combining Agentic AI based test generation, executable test creation, deterministic validation, human oversight, automated execution, and continuous feedback, the implementation reduced repetitive testing effort while improving coverage and traceability.
The engagement enabled the client’s QA teams to spend less time on test authoring and maintenance and more time on validation, defect analysis, edge cases, and continuous quality improvement.