Scenario Generator
Creates realistic and adversarial test scenarios across accuracy, hallucination, prompt injection, incomplete context, angry users and business-specific questions.
02 / PROJECTBUILT SYSTEM
AGENTIC AI · AUTOMATED EVALUATIONAutonomous AIQA Agent.
A multi-stage AI testing system built to generate scenarios, execute live conversations, evaluate responses, detect failures and produce structured QA reports without relying on slow manual testing.
autonomous-qa-agent.workflow
RUNNING
01
Planner Agent
Generate test scenarios
02
Test Runner
Execute live conversations
03
Judge Agent
Score and classify failures
04
QA Report
Actionable engineering output
100–1000tests per day
8+evaluation criteria
Liveassistant testing
PASS RATE94.6%Latest evaluation batch
ISSUE FOUNDRetrieval mismatchSeverity: Medium
01PROJECT OVERVIEW
Manual chatbot testing does not scale.This system does.
A single tester can manually validate only a limited number of conversations each day. That makes comprehensive regression testing slow, inconsistent and difficult to repeat.
This agentic QA system automates the complete evaluation loop. It creates diverse test scenarios, sends them to the target AI assistant, judges the resulting answers and converts failures into structured engineering feedback.
TESTING CAPACITY100–1000
automated test executions per day
MANUAL BASELINE50–70
questions per tester per day
OUTPUTStructured
scores, severity, reasons and fixes
SYSTEM ARCHITECTURE
Four specialised stages.One automated evaluation loop.
Each stage performs one controlled responsibility, making the workflow easier to debug, improve and maintain.
Live Test Runner
Sends each scenario into the target assistant and captures the complete response, timing, execution status and supporting metadata.
AI Judge
Evaluates each response against defined quality criteria, identifies failures and assigns a structured score, severity and explanation.
Reporting Layer
Transforms raw evaluations into actionable QA reports showing recurring failures, risk areas, performance trends and recommended fixes.
EVALUATION FRAMEWORK
The judge does more than label an answergood or bad.
Every response is inspected across business relevance, factual accuracy, hallucination safety, retrieval quality, clarity, CTA behaviour and resistance to malicious prompts.
QA EVALUATION RESULT82 / 100
AccuracyPASS
Business relevancePASS
Retrieval qualityWARNING
Prompt-injection resistancePASS
IDENTIFIED ISSUE
The answer was correct but used a weak case-study match. Improve retrieval filters and rank results by intent relevance.
CORE CAPABILITIES
What the system handles.
01
Automated test generation
02
Live conversation execution
03
Response scoring
04
Hallucination detection
05
Prompt-injection testing
06
Failure severity classification
07
Actionable QA reporting
08
Human review escalation
TECHNOLOGY
Tools and concepts used.
Agentic AILLM Evaluationn8nGeminiAPIsPrompt EngineeringStructured OutputsAutomated Reporting
ENGINEERING PRINCIPLE
AI outputs are never trusted blindly. Every stage uses structured data, validation rules, controlled prompts and explicit failure handling.
AI systems need evaluationbefore they need scale.
This project demonstrates how Adarsh approaches AI reliability: automate repeatable work, separate responsibilities, validate every output and convert failures into measurable improvements.
Interview AdarshOS↗