Drop your answers here! 

Title: Astra-Perf: The Autonomous SAP Performance Intelligence Agent

1. What will it do? (Business & Professional Value)

Astra-Perf is an autonomous Performance Engineering agent designed to move beyond simple script execution. It performs Real-time Root Cause Analysis (RCA) during distributed load tests in SAP environments.

Business Value: It reduces "Mean Time to Repair" (MTTR) by 80% by identifying whether a performance bottleneck is at the ABAP code level, database layer, or infrastructure, without manual log digging.

Professional Value: It transforms QA from a reactive role to "Predictive Performance Engineering."

2. When will it operate?

It operates in a Semi-Autonomous, Continuous cycle. It triggers automatically post-deployment in the staging environment and runs parallel to the performance test suite.

3. Where is it located in the SDLC/STLC?

It is embedded within the STLC (Performance Testing Phase) and integrated directly into the CI/CD Pipeline via Jenkins (utilizing SAP ECT Integration).

4. Who controls or audits it?

The QA Performance Lead (Human-in-the-loop) audits the agent. Astra-Perf provides high-fidelity reports and "Confidence Scores" for its findings, but the final Go/No-Go decision for production remains with the human expert.

5. How will it work? (The Architecture)

Brain (LLM): Uses advanced LLMs to interpret SAP system traces and performance logs.

Memory: Uses a Vector Database (RAG) to store historical performance data, allowing the agent to compare current anomalies with past known issues.

Tools/APIs: Connects via APIs to Tricentis NeoLoad and SAP Solution Manager to pull telemetry data in real-time.

Bonus Points Section:

Agent Name: Astra-Perf v5.1

Failure Modes & Protection:

Risk: "False Positives" due to temporary network jitter.

Protection: I’ve implemented a Statistical Guardrail Layer. The agent cross-references telemetry with baseline history; if the deviation is within a standard noise threshold, it flags it as "Warning" rather than "Critical," preventing unnecessary pipeline blocks.

Concept Proof: Currently integrating this with Jenkins-based distributed load testing to ensure enterprise-grade scalability.

Who

Primary users: QA testers, QA leads, compliance auditors
System involved: SAP systems (e.g., ECC, S/4HANA)
Agent role: AI-powered QA automation agent supporting testers and compliance teams

What

An intelligent QA agent that:

Reads test plans and test scripts
Identifies test data created by testers
Executes or observes SAP transaction codes (T-codes)
Automatically captures screenshots for each test step
Generates test execution documentation aligned with compliance standards

When

During manual or semi-automated test execution
Primarily used in:

System Integration Testing (SIT)
User Acceptance Testing (UAT)
Regression testing
Audit and compliance validation cycles

Where

Within SAP environments (GUI, Web GUI, Fiori)
Integrated with:

Test management tools (e.g., SAP Solution Manager, ALM tools)
Documentation repositories (e.g., SharePoint, Confluence, DMS)

Why

To:

Reduce manual effort in test evidence collection
Ensure audit-ready compliance documentation
Improve consistency and accuracy of test execution records
Speed up testing cycles while meeting regulatory standards (SOX, GxP, ISO, etc.)

How

The agent:

Parses test plans and scripts to identify steps and related T-codes
Retrieves test data associated with each test case
Navigates SAP screens based on T-codes and execution flow
Automatically captures screenshots at each relevant step
Maps screenshots to test steps and generates structured execution documentation
Stores artifacts in a compliance-aligned format for audits and reviews

I have designed a Test planner agent that will read all the test cases and based on the preconditions, workflow, login user roles and few more parameters, agent would assess them and will generate different sets of test cases. This will help me to assign the sets to a particular user so that they are working on logically divided scope without overlap. 

• What will the agent do? - Test plan
• Why should this be an agent? (Business value + Practitioner value) - Eases Test plan effort and will be of real help for larger teams
• When – What agency level will it operate at? Rule based
  (Rule-based, Workflow, Semi-autonomous, Autonomous)
• Where will it fit in your SDLC  / STLC? STLC
• Who controls or reviews it? Test Lead
• How will it roughly work? (LLM? APIs? Tools? Deterministic logic? Memory?) Tools and LLM

Output: It will provide the reason what parameters it has chosen to place it test cases in a particular set, provide the test cases that were not part of any set for me to verify manually and assign them. 

I have implemented it with Cursor Rules with predefined folder structure and MCP integration with Testcase management tool

Flaky E2E failures in CI (wasting 1–2 hrs daily on triage).

 Agent Name: FlakeSherlock

1️⃣ What will the agent do?

• Trigger automatically on CI test failure

• Analyze logs + last 20 historical runs

• Inspect PR diff for risky changes

• Classify failure as:

  • Product Bug

  • Flake

  • Infra Issue

  • Test Issue

• Post PR comment with:

  • Root cause 

  • Confidence score

• Auto-label failure in CI

2️⃣ Why should this be an agent?

💼 Business Value

• Cleaner CI signal

• Faster release cycles

👨‍💻 Practitioner Value

• Eliminates repetitive triage

• Reduces burnout

• Builds reusable historical failure memory

3️⃣ Agency Level

Semi-autonomous (As discussed in our meetup)

• Suggests classification

• Applies labels

4️⃣ Where in SDLC / STLC?

CI Failure → FlakeSherlock → PR/Jira Summary → Human Review

Sits between failure detection and manual triage.

5️⃣ Ownership

• SDET team owns agent logic

• QA lead defines thresholds

• Developers review output in PR

6️⃣ How it works

• Deterministic log pattern matching

• LLM-based reasoning + classification

• Vector memory of historical failures

• Integrations: CI + Git + Jira APIs

AI Agent Use Case for Software Testing

Using the 5W1H Framework

 Real Testing Problem

In my current QA work, a recurring challenge is manually creating and updating test cases after every feature or ticket change.

Each sprint requires:

• Reading Jira tickets
• Understanding acceptance criteria
• Identifying edge cases
• Updating regression coverage
• Creating new test scenarios

This is repetitive, time-consuming, and prone to missed coverage.

 Agent Name

TestSage – A lightweight AI assistant that helps QA engineers design and update test coverage from tickets and feature changes.

WHAT will the agent do?

The agent will:

• Read Jira ticket descriptions and acceptance criteria
• Extract feature changes and risks
• Generate suggested:
  • test scenarios
  • edge cases
  • regression impact areas
  • API test ideas
  • automation candidates
• Compare with existing test cases
• Suggest updates to the regression suite

The agent does not auto-execute tests.
It assists the QA in thinking and planning.

 WHY should this be an agent?

    Business Value

• Faster test design
• Better coverage
• Fewer missed edge cases
• Reduced regression escapes
• Shorter release cycles

   Practitioner Value

• Saves 1–2 hours per ticket
• Reduces repetitive manual work
• Helps junior QA think more strategically
• Improves consistency in regression planning

This is a high-value, low-risk automation opportunity.

   WHEN — Agency Level

Semi-Autonomous Agent

The agent suggests outputs, but QA reviews and approves them.

Human-in-the-loop is required.

  WHERE in SDLC / STLC?

The agent fits into the test design and regression planning phase.

Flow:
Dev updates ticket →
Agent analyzes →
QA reviews suggestions →
Tests updated →
Execution begins

Used during:

• Sprint planning
• Feature refinement
• Regression preparation

 WHO controls or reviews it?

Primary reviewer: QA Engineer
Secondary reviewer: QA Lead

The agent cannot:

• Automatically update test cases
• Push changes to TestRail
• Modify regression suite without approval

All outputs require human review.

  HOW will it work? (Technical Overview)

Inputs

• Jira ticket text
• Acceptance criteria
• PR description
• Existing test cases

Processing

• LLM for reasoning and scenario generation
• Deterministic rules for structure
• Risk tagging logic
• Optional memory of previous features

Tools/Stack

• LLM (OpenAI or similar)
• Python script
• Jira API
• Test management tool API
• Prompt templates

Output

• Suggested test cases
• Regression impact list
• Risk areas
• Automation candidates

Delivered as a Markdown or report for QA review.

  Failure Modes & Guardrails

1. Hallucinated test scenarios

Risk: Agent invents unrealistic cases
Guardrail:

• Must reference ticket content
• Confidence score
• Mandatory QA review

2. Too many low-value test cases

Risk: Over-testing
Guardrail:

• Risk-based prioritization
• Tag critical vs optional

3. Wrong regression mapping

Risk: Suggests irrelevant tests
Guardrail:

• Tag-based mapping
• Suggestion-only mode

4. Security & data access

Risk: Sensitive ticket data exposure
Guardrail:

• Internal deployment
• Limited API permissions

 Lightweight Prototype Idea (Bonus)

A simple Python script can be built to:

1. Pull a Jira ticket
2. Send text to an LLM
3. Generate:
   • test scenarios
   • edge cases
   • regression impact
4. Output a QA review report

Architecture (simplified):

Jira → Agent → LLM → Test suggestions → QA review → Test suite update

This could be implemented as a weekend proof-of-concept.

 Summary

TestSage is a semi-autonomous AI QA assistant that helps generate and update test coverage from feature changes.
It reduces repetitive work, improves coverage quality, and keeps QA engineers in full control of decisions.

This use case is practical, low-risk, and directly applicable to real sprint workflows.

Agent Name: TestSage

In my current testing workflow, a recurring challenge is creating high-quality test cases from changing requirements (Jira tickets, PRDs, API specs).
This task is:

• repetitive

• time-consuming

• error-prone

• dependent on individual tester experience

What will the Agent Do?

TestSage automatically analyzes requirements (Jira story, API spec, or PRD) and generates:

• Positive test cases

• Negative scenarios

• Boundary tests

• Edge cases

• Data validation scenarios

• API test payload variations

It also flags:

• missing acceptance criteria

• ambiguous requirements

• testability risks

Why should this be an agent? (Business Value + Practitioner value)
Business Value

• Faster test readiness → shorter release cycles

• Reduced defect leakage

• Standardized test coverage across teams

Practitioner Value

• Saves 60–70% test design time

• Reduces mental fatigue from repetitive scenario thinking

• Helps junior testers design expert-level test cases

When - What agency level will it operate at?

Semi-Autonomous Agent

Why not fully autonomous?
Because test design still requires human validation for business logic accuracy.

Workflow:
Agent generates → Tester reviews → Tester approves → Stored in Test Management Tool

Where will it fit in your SDLC/STLC?

Phase: Test Design + Requirement Analysis

Integration Points:

• Jira  → input source

• TestRail → output storage

• Git PR comments → optional requirement source

Who controls or reviews it?

Primary reviewer → QA Engineer
Secondary reviewer → QA Lead (optional)

The agent never pushes tests directly without approval.

How will it work?

1. LLM Engine → requirement understanding

2. Rules Engine → test template formatting

3. API Layer → Test tool integration

4. Memory Layer → stores past test patterns

5. Validator → checks duplicates + coverage gaps

Good Day ​@PolinaKr , ​@Mustafa 

Here is my response.

AI Agent Use Case in Testing — “TestCase Genie”

The Real Problem (from my work)

In my day-to-day testing work, test case creation and maintenance is a recurring pain.

• Requirements change frequently
• Manual test case writing is time-consuming
• Coverage gaps happen easily
• Review cycles take too long
• Duplicate or low-value test cases slip in

This is not a one-time problem, it happens every sprint.

Agent Name: TestCase Genie

A lightweight AI agent that generates, reviews, and improves test cases from requirements automatically, while keeping humans in the loop.

5W1H Framework

WHAT will the agent do?

TestCase Genie will:

• Read user stories / requirements
• Generate structured test scenarios
• Create positive + negative test cases
• Suggest edge cases using heuristics (RCRCRC, SFDIPOT)
• Flag duplicate or weak test cases
• Provide coverage summary

Output: Ready-to-review test cases in standard QA format.

WHY should this be an agent?

Business Value

• Faster test design → reduces sprint delays
• Better coverage → fewer production defects
• Consistent test quality across teams
• Reduced manual effort → cost savings

Practitioner Value (very real)

As a tester, this removes the most repetitive part of my work:

• No more blank-page syndrome
• Faster first draft of test cases
• Helps junior testers ramp up quickly
• Improves thinking about edge cases

Important: Agent assists — not replaces — the tester.

WHEN — Agency Level

Level: Semi-Autonomous Agent

Why not fully autonomous?

• Test design still needs human judgment
• Business context matters
• Risk assessment is human-driven

Agent responsibilities

• Generates
• Suggests
• Flags issues

Human responsibilities

• Reviews
• Approves
• Edits critical scenarios

This keeps the system safe and trustworthy.

WHERE in SDLC / STLC?

Primary fit:

• Test Design Phase

• Sprint Planning

• Requirement Analysis

Workflow position:

User Story Ready
      ↓
TestCase Genie runs
      ↓
QA Review
      ↓
Approved Test Cases → Test Execution

WHO controls or reviews it?

Primary reviewer: QA Engineer / SDET
Secondary visibility: Test Lead

Governance model

• Agent cannot push directly to production test suite
• Human approval mandatory
• Review checklist enforced

This prevents blind trust in AI.

HOW will it work? (Architecture)

Core Components

       1.LLM Layer

• Requirement understanding
• Test case generation
• Edge case reasoning

      2. Deterministic Logic

• Template enforcement
• Duplicate detection
• Coverage scoring
• Rule checks

3. Tools / Integrations

• Jira API → fetch user stories
• Test management tool (TestRail / Zephyr)
• Playwright repo (optional future step)

4. Memory

• Past approved test cases
• Project domain context
• Known defect patterns

Rough Architecture Sketch

[Jira User Story]
        ↓
   Agent Trigger
        ↓
 +------------------+
 |   LLM Engine     |
 | - Scenario gen   |
 | - Edge cases     |
 +------------------+
        ↓
 +------------------+
 | Deterministic    |
 | Validators       |
 | - Template check |
 | - Duplicate scan |
 +------------------+
        ↓
   QA Review UI
        ↓
 Approved → Test Repo

Failure Modes & Guardrails

This is where experienced testers think carefully.

Failure Mode 1: Hallucinated test cases

Risk: AI invents flows not in requirements

Guardrails:

• Requirement grounding prompt
• Confidence scoring
• Human review mandatory
• Traceability matrix check

Failure Mode 2: Superficial coverage

Risk: Looks good but misses edge cases

Guardrails:

• Force heuristics (RCRCRC, SFDIPOT)
• Coverage scoring
• Risk-based prompts
• Review checklist

Failure Mode 3: Duplicate test cases

Risk: Test suite bloat

Guardrails:

• Semantic similarity check
• Hash-based duplicate detection
• Merge suggestions

Failure Mode 4: Over-automation trust

Risk: Team blindly accepts AI output

Guardrails:

• Human approval gate
• Audit logs
• “AI-generated” tagging
• Periodic quality review

Lightweight Prototype Idea - Simple PoC (what I am building)

• Input: Jira story text
• Tool: Python + OpenAI + prompt templates
• Output: Structured test cases (CSV/Markdown)
• Optional: Playwright test skeleton generation

Example Agent Snippet (conceptual)

def generate_test_cases(user_story):
    scenarios = llm.generate_scenarios(user_story)
    edge_cases = llm.apply_heuristics(user_story)
    
    validated = validator.check_duplicates(
        scenarios + edge_cases
    )
    return validated

Why this use case is practical

This is not sci-fi.

This solves a real sprint bottleneck that every QA team faces:

• Repetitive
• Time-consuming
• Error-prone
• High ROI if improved

Thanks,

Ramanan Prabakaran

Agent Name - ConfluJira Impact Assistant 

1. What will it do?

When CR is updated or added in Confluence, the agent will

a. Extract:

- Requirement description 

- Key Changes

- Discussion comments 

- Action items

b. Fetch related:

- Jira user stories

- Linked test cases

c. Compare: 

- Updated requirement vs existing test cases

d. Generate:

- List of impacted test cases

- Missing scenarios 

- Clarification questions 

- Suggested regression scopes

e. Post:

- Draft impact analysis comment in Jira

- Or summary for QA review 

2. Why should this be an agent?

Business Value: 

- Reduces risk of missing scenarios discussed in meeting

- Improves traceability between Confluence and Jira

- Standardizes Impact analysis

- Speeds up CR validation 

Practitioner value:

- Saves effort switching between tools 

- Captures discussion points that may be forgotten 

- Improve regression confidence 

3. When - What age cy level?

Work-flow level (Semi- Autonomous)

It triggers whe CR update happens or CR status change in Jira.  It does update the test case automatically. It will give suggestions.

4. Where will it fit in SDLC / STLC?

SDLC phace - Requirement analysis and review 

STLC phase-

Requirement review 

Test Impact Analysis 

Regression planning

5. Who controls or review it?

Primary reviewer - Me (QA)

Secondary reviewer- BA

Human validation mandatory

6. How will it roughly work?

Tools used

- Confluence API

- Jira API

- LLM (copilot)

ScopeRadar — Impact-Aware Regression Scoping Agent

I have watched this happen across multiple sprints.
The ticket looks small. The impact isn’t.

The Real Problem

In async fintech systems, a small requirement change rarely has a small impact.

A retry rule changes. The Jira ticket looks minor. But it silently touches four downstream services, two event queues, idempotency handling, and eleven legacy test cases. You only discover the blast radius after something leaks to production.

This is not a test case generation problem.
It is a scoping intelligence problem.

Today, the tester is forced to choose between:

• Over-testing — two to three hours lost

• Under-testing — production regression

In payment systems, under-testing equals revenue leakage.

ScopeRadar exists to remove that guesswork.

WHAT will it do?

ScopeRadar ingests a Jira diff or PR delta and produces a structured impact report covering:

• Impacted test cases via tag and service graph mapping

• Missing regression coverage gaps

• Affected services and async flows

• Risk classification: Low, Medium, High

• A regression confidence score

Sample Output

Changed Rule: Retry window extended
Affected Services: Payment Processor, Settlement Handler
Impacted Tests: TC-245, TC-312, TC-411
Missing Scenario: Delayed webhook retry after partial failure
Risk Level: High
Confidence: 82%

It does not auto-execute tests.
It does not modify regression suites.
It does not make release decisions.

It informs. Humans decide.

WHY an agent and not just a script?

A script can map file changes to test tags.

It cannot detect that changing a retry window from three to five implicitly shifts settlement timing and invalidates an idempotency assumption inside TC-411.

That requires semantic reasoning over business rules, not just code diffs.

Additionally:

• Context shifts every sprint

• Fragile areas evolve

• Historical change-to-defect patterns matter

ScopeRadar combines deterministic mapping with semantic reasoning and memory.

The ROI is not faster test writing.
It is faster, safer regression scoping decisions — the real sprint bottleneck.

WHEN — Agency Level

Semi-Autonomous.

ScopeRadar suggests.
QA decides.

Why not autonomous?

• Business intent cannot be fully inferred

• Regulatory environments require human accountability

• Risk appetite varies per release

WHERE in SDLC

Requirement Update
→ ScopeRadar Analysis
→ QA Impact Review
→ Regression Planning
→ Execution

It sits exactly between impact analysis and regression planning — where guesswork currently lives.

It is pre-execution intelligence.

WHO controls it?

Primary reviewer: QA or SDET
Secondary visibility: Backend Engineer and Tech Lead

Release decisions remain fully human-controlled.

HOW — Architecture

Layer 1 — Deterministic

• Module-to-test tag mapping

• Service dependency graph

• Event flow mapping (queue → consumer → DB)

• PR file change tracking

Purpose: Ground reasoning and prevent hallucination.

Layer 2 — LLM

• Detect semantic business rule changes

• Identify behavioral impact

• Interpret ambiguous requirement wording

Purpose: Augment reasoning, not replace deterministic logic.

Layer 3 — Memory

• Historical change-to-defect correlations

• Known fragile async areas

• Past timing-related incidents

Purpose: Improve scoping accuracy over time.

Failure Modes and Guardrails

Over-scoping everything
Addressed through confidence scoring and Strong Impact versus Possible Impact tiers.

Missing indirect async dependencies
Addressed through the dependency graph and event producer-to-consumer cross-checking.

Over-trusting the agent
Addressed through suggestion-only mode and mandatory QA sign-off.

LLM misreading business nuance
Addressed by ensuring the deterministic layer runs first and requiring the LLM to reference diff evidence in its output.

Why This Works

Most AI-in-testing ideas optimize test writing.

ScopeRadar optimizes uncertainty reduction.

In async fintech systems, that is where sprint velocity actually collapses.

This is not automation for convenience.
It is automation for risk containment.

AI Agent Use Case – “ReproGenie”

Real Testing Problem - Recurring Pain:

Writing high-quality, reproducible bug reports from exploratory or regression testing sessions.

Especially in:

• Complex business logic
• Mobile UI issues
• API mismatches between Android & iOS
• Edge-case failures after regression runs

Common issues:

• Steps are incomplete
• Logs/screenshots not properly attached
• Environment details missing
• Reproducibility inconsistency
• Back-and-forth with devs

5W1H Framework

WHAT – What will the agent do?

ReproGenie will:

• Convert raw tester inputs (notes, logs, screen recordings, console output)
• Into a clean, structured, dev-ready bug report

It will:

• Extract reproduction steps
• Detect missing info
• Identify environment details
• Suggest expected vs actual behavior
• Classify severity
• Attach relevant logs
• Cross-check if similar bug exists
• Suggest possible impacted modules

WHY – Why should this be an agent?

Business Value

• Faster bug resolution
• Reduced dev clarification loops
• Cleaner Jira backlog
• Improved sprint predictability
• Better regression traceability

Practitioner Value (YOU)

As a tester:

• Saves 20–30% reporting time
• Improves credibility
• Reduces cognitive load after long test cycles
• Maintains consistency across releases
• Helps junior testers improve quality

Especially useful in:

• Complex feature areas
• Multi-platform testing
• Animated UI automation issues

WHEN - Agency Level?

Semi-autonomous Agent

Why not fully autonomous?

Because:

• Bug severity sometimes needs human judgment
• Business context matters
• Reproducibility must be verified

So flow is:

• Tester → Agent draft → Tester review → Submit

WHERE – Where in SDLC/STLC?

It fits in:

• During Exploratory Testing
• During Regression Testing
• After Automation Failures
• During UAT bug triage

Specifically:

• Between Test Execution → Defect Logging

WHO – Who controls or reviews it?

Primary: QA Engineer

Secondary:

• QA Lead
• Product Owner (if high severity)

Agent never submits automatically without review.

HOW – Rough Architecture

Core Components

1. LLM Layer
   • Parses natural tester notes
   • Extracts structured steps
   • Rewrites for clarity
2. Deterministic Layer
   • Template enforcement
   • Severity matrix logic
   • Required fields validation
   • Duplicate check logic
3. Tools & APIs
   • Jira API
   • Appium logs ingestion
   • Android logcat parsing
   • API response capture
   • Git commit linking
4. Memory
   • Stores:
     • Past bugs
     • Similar module failures
     • Known flaky areas
   • Improves classification over time

Agent Name

ReproGenie

Architecture Sketch (Lightweight)

Tester Input (Notes / Logs / Screenshot)
          ↓
Input Parser
          ↓
LLM (Structure + Clarify + Improve)
          ↓
Validation Engine (Missing info? Required fields?)
          ↓
Duplicate Detector (Jira API check)
          ↓
Severity Engine (Rule + Context based)
          ↓
Draft Bug Report
          ↓
QA Review → Submit

Failure Modes & Guardrails

Mini Prototype Snippet (PoC Idea)

Example prompt structure:

prompt = f"""
You are a QA assistant.

Convert the following raw tester notes into a structured bug report.

Notes:
{tester_notes}

App Version:
{version}

Environment:
{environment}

Ensure:
- Clear reproduction steps
- Expected vs Actual
- Pre-conditions
- Attach log suggestions
- No hallucinations
"""

Enhancement:

• Add Jira API integration
• Add log similarity detection
• Add severity rule engine

Advanced Version (Future Roadmap)

• Auto-watch failed Appium test runs
• Convert failure stack trace → human-readable repro
• Identify flaky vs real bug
• Suggest impacted regression areas
• Generate negative test cases automatically