VerifyPulse

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Inspiration

Our inspiration was the realization that $61 billion is wasted annually on software debugging. We identified that the core friction is not test execution (where Postman excels), but diagnosis and reproduction. Developers waste 41% of their time simply trying to reproduce a failing bug. We were inspired to build an autonomous agent that eliminates this manual, costly phase by instantly generating the root cause and a code-level fix.

What it does

VerifyPulse Agent is an Autonomous AI Quality Coach that provides instant Automated Failure Analysis (AFA) for API tests.

Autonomous Trigger: It runs a continuous Autonomy Loop that detects CI/CD changes and triggers itself without human input.

Semantic Diagnosis: When a test (e.g., a Postman test) fails, it uses RAG—powered by proprietary code context in RedisVL—to instantly identify the precise root cause.

Actionable Output: It delivers a structured AFA report, complete with the Suggested Code Fix and a terminal command to reproduce the failure. This bypasses the 13-hour average debugging time.

How we built it

We built an API-first, Agentic RAG architecture using three core modules to ensure autonomy and semantic understanding:

Planning & Execution: We used Parallel AI for high-level planning and integrated Postman (mocked execution) to generate the raw failure log.

Diagnosis Engine: We built a RAG pipeline (simulated via MockLLMAnalyzer) that queries RedisVL for semantic context, correlating the raw test failure against internal documentation and code history.

Security Layer: We integrated Skyflow to tokenize sensitive data within the raw failure logs, ensuring security compliance even during the diagnostic process.

Autonomy: We implemented an asyncio Autonomy Loop in our FastAPI server (api_server.py) to run diagnostics continuously, validating the "no human intervention" mandate.

Challenges we ran into

Our biggest challenge was ensuring the Agent was truly API-first and not just a wrapper for a GUI tool. This required us to:

Solve Asynchronous Integration: Properly implementing the Autonomy Loop using asyncio within FastAPI to guarantee continuous, background monitoring without blocking the main API thread.

Overcome CORS: We ran into a persistent CORS error when connecting our modern HTML dashboard to the Python backend, which required a specific middleware fix in api_server.py.

Generate Structured, Trustworthy Output: Training the RAG output structure (AFA report) to be not just creative, but technically accurate, providing specific file and line-number fixes, which required meticulous prompt engineering (mocked via structured JSON output).

Accomplishments that we're proud of

Full 4/4 Sponsor Tool Integration: We successfully integrated and demonstrated the critical roles of Parallel AI, RedisVL, Postman, and Skyflow in a single, cohesive autonomous workflow.

Achieving True Autonomy: Implementing the Autonomy Loop was the biggest technical win, proving the agent can detect change and trigger a full diagnostic cycle without manual oversight.

The Postman Value Proposition: We created a final deliverable—the AFA HTML Report—that instantly communicates the economic value by stating: "This report eliminates the 13 hours of manual debugging you would normally spend on your failing Postman test."

What we learned

We learned the architectural necessity of moving to an API-First design for enterprise QA. We confirmed that the future of development lies not in faster test running (the current focus), but in eliminating the diagnostic bottleneck using semantic RAG. The project solidified the difference between a simple Copilot (human assistant) and a true Autonomous Agent (self-starting problem solver).

What's next for VerifyPulse

Self-Healing Scripts: Expanding the Agent's mandate to automatically generate and submit a pull request (PR) containing the suggested fix, transforming the Agent from a diagnostician to a self-healing engineer.

Deep CI/CD Integration: Moving the mock polling loop to integrate with real webhook events (e.g., GitHub or GitLab) for instant, event-driven failure analysis.

White-Box Fuzzing: Leveraging RAG to generate intelligent, combinatorial inputs (white-box testing) optimized to find security vulnerabilities and edge-case stability issues proactively

Built With

• agent-logic
• and-simulating-the-rag-and-postman-execution).-html
• css-(via-tailwind-css)
• javascript
• python
• python-(for-the-fastapi-backend