Codecademy Agentic AI Bootcamp · RAG-Powered

Every QA deliverable.
Generated in seconds.

Test cases, bug reports, traceability matrices, test plans — grounded in ISTQB and IEEE 829, exported to Jira, ready to ship.

See it in action How it works
14+
Deliverable types
ISTQB
Foundation grounded
IEEE 829
Doc standard
STLC
Full lifecycle

Live preview

What your team actually sees

A structured QA workspace that produces real deliverables — not generic chatbot answers.

QA
QA Nexus Agent
● Online · ISTQB grounded
Defect Mode
U
I need a bug report for a login failure — users are getting a 500 error when submitting valid credentials on the mobile app.
QA
On it. Based on the symptom pattern, this is a P1/Critical defect — a 500 on a core auth flow blocks all mobile users. Here's the IEEE 829-structured bug report:
📋 Defect Report — DEF-0047
FieldValue
TitleMobile login returns HTTP 500 on valid credentials
SeverityCritical
PriorityP1 — Blocker
EnvironmentMobile App v2.4 · iOS 17 / Android 14
Steps to reproduce1. Open app → 2. Enter valid credentials → 3. Tap Sign In → 4. Observe 500 error
ExpectedUser is authenticated and redirected to dashboard
ActualHTTP 500 Internal Server Error returned; user remains on login screen
Assigned toDev Team — Backend
IEEE 829 · Defect Template ISTQB · Severity Guide STLC · Defect Lifecycle
U
Can you also generate test cases for the login module?
QA
Here are test cases covering positive, negative, and boundary scenarios using equivalence partitioning:
🧪 Test Cases — Login Module
TC-IDScenarioPriority
TC-L01Valid email + valid password → successful loginHigh
TC-L02Valid email + wrong password → error message shownHigh
TC-L03Empty fields → inline validation triggeredMedium
TC-L04SQL injection in email field → input sanitizedCritical
TC-L055 consecutive failed logins → account lock triggeredHigh
TC-L06Session persistence after browser refreshLow
ISTQB · Equivalence Partitioning ISTQB · Boundary Value Analysis IEEE 829 · Test Case Spec
Ask about test planning, RTM, exit criteria…

What's inside

Built for real QA work

Not a generic chatbot. A structured QA system grounded in industry methodology.

ISTQB-grounded retrieval

Every answer retrieves from a curated knowledge base of ISTQB Foundation, IEEE 829, and STLC methodology — with transparent source citations on every reply.

RAG · Transparent sourcing

Target real applications

Paste any URL and the agent attempts to fetch the page, extract its forms/buttons/fields, and generate site-specific test cases referencing actual elements.

CORS-aware fetch

14+ deliverable types

Test plans, test cases (tabular & Gherkin), RTM, bug reports, test summary reports, exit criteria checklists — all structured to standard.

IEEE 829 · ISTQB

Full STLC coverage

From requirements review and test planning through defect triage and test closure — the agent follows the complete software testing life cycle.

Requirements → Closure

Jira-ready export

Test cases and bug reports export as Jira-importable CSV with priority mapping, smart filenames, plus TXT and HTML for any other tool.

CSV · Direct import

Resizable workspace

Drag dividers between panels to widen the chat or shrink the sidebars. Layout preferences and theme persist across sessions.

Light & Dark mode

Deliverables

Everything your QA team needs

One agent. Every document. ISTQB and IEEE 829 quality every time.

Planning
Test Plan
Planning
Risk-based Strategy
Requirements
Testability Checklist
Requirements
Traceability Matrix
Design
Test Cases (Tabular)
Design
BDD / Gherkin Scenarios
Design
Boundary Value Sets
Design
Decision Tables
Execution
Test Run Summary
Execution
Daily Status Email
Defects
Bug Report (IEEE 829)
Defects
Severity & Priority Triage
Closure
Test Summary Report
Closure
Exit Criteria Checklist
Closure
Lessons Learned Doc

Under the hood

How the retrieval actually works

Not a wrapper around a prompt — a real RAG pipeline you can watch in every answer.

1
Knowledge base
A curated QA methodology library — ISTQB Foundation Level, IEEE 829 documentation standard, STLC phase definitions, test design techniques, and defect lifecycle — written as small, source-tagged chunks.
ISTQB · IEEE 829 · STLC · Agile QA
2
Retrieval
Your question is scored against every chunk using semantic similarity. Only the most relevant methodology fragments are pulled in as context for this specific query — not the entire library.
Semantic search · Relevance scoring
3
Target context
If you've set a target URL, the agent fetches the page, extracts its forms, buttons, and structure, and includes that in the prompt — so deliverables reference actual elements from your app.
URL-aware · CORS-resilient
4
Grounded generation
The model answers using only the retrieved chunks and target context — keeping every response anchored to real QA methodology and your actual application.
No hallucination
5
Sources shown
Every reply lists the exact methodology chunks it retrieved — ISTQB section, IEEE clause, or STLC phase — so you can always verify why the agent said what it said.
Auditable · Transparent