Where Quality is Forged, Not Just Checked

Real implementation stories from the trenches. Classical QE practices evolved with PACT principles. No hype, no vendor speak—just what actually works in production.

25+
Years Experience
PACT
Framework
100%
Production Tested
Open
Source First

The Forge Philosophy

From Testing to Orchestration

Quality isn't tested in—it's built in. We're moving from testing-as-activity to agents-as-orchestrators, bridging classical QE with agentic intelligence through PACT principles.

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Practitioner First

Everything here is battle-tested in production. Real implementations, actual failures, honest lessons. No ivory tower theory—just what works (and what doesn't) from someone in the trenches.

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Community Driven

Building the Serbian Agentic Foundation and sharing globally. Learning happens together—through meetups, open source, and honest conversations about what quality means in the AI age.

From the Forge

View all 8 articles →
● Published Honest Failure Quality Engineering

The Five-Release Journey Where I Forgot to Be a Tester

How a quality engineering professional shipped broken features for 17 days while claiming "100% complete." Eight brutal lessons learned from forgetting to verify what I already knew how to test.

Published November 13, 2025 30 min read
● Published Production Story Release 1.4.2

Show Me the Data: How One Question Exposed Release 1.4.2's Hidden Flaws

When stub tests pass CI but test nothing, and agents report success on broken code. A journey from false confidence to verified truth, discovering that "show me the data" cuts through every illusion.

Published November 3, 2025 18 min read
● Published Framework Deep Dive

Holistic Testing in the Agentic Age

How the Holistic Testing Model evolves when testing happens across boundaries, in production, and through autonomous agents. From shift-left to orchestrated quality.

Published October 15, 2025 16 min read
// AQE Fleet v1.4.4 - 19 Specialized Agents
const fleet = {
  core: [
    "test-generator",      // AI-powered test creation
    "test-executor",       // Multi-framework execution
    "coverage-analyzer",   // O(log n) gap detection
    "quality-gate",        // ML-driven validation
    "quality-analyzer",    // ESLint, SonarQube, Lighthouse
    "complexity-analyzer"  // Cyclomatic complexity
  ],
  performance: [
    "performance-tester",  // k6, JMeter, Gatling
    "security-scanner"     // SAST, DAST scanning
  ],
  strategic: [
    "requirements-validator",    // INVEST + BDD
    "production-intelligence",   // Incident replay
    "fleet-commander"            // 50+ agent coordination
  ],
  advanced: [
    "regression-risk-analyzer",  // ML test selection
    "test-data-architect",       // 10k+ records/sec
    "api-contract-validator",    // Breaking changes
    "flaky-test-hunter"          // 100% ML accuracy
  ],
  specialized: [
    "deployment-readiness",      // Multi-factor risk
    "visual-tester",             // AI-powered UI diff
    "chaos-engineer"             // Resilience testing
  ]
};

// Key Metrics: 70-81% cost savings • 34 QE skills
// 6 frameworks • 54 MCP tools • Q-Learning enabled
v1.4.4 • TypeScript + Node.js 20+ • MIT License

AQE Fleet: 19 Specialized Agents

Enterprise-grade agentic testing framework with 19 specialized AI agents achieving 70-81% cost savings. Built with TypeScript, featuring Q-learning, multi-model routing, and 34 world-class QE skills.

70-81% Cost Savings

Multi-model router with intelligent selection (GPT-3.5, GPT-4, Claude Haiku, Claude Sonnet 4.5)

Q-Learning & Pattern Bank

20% continuous improvement with 85%+ pattern matching across 6 frameworks

34 QE Skills Library

Comprehensive skills covering shift-left/right, accessibility, mobile, database, and chaos engineering

LionAGI QE Fleet: Python Orchestration

Python reimplementation of the AQE framework using LionAGI orchestration. 18 specialized agents with async-first architecture, alcall integration, and 82% test coverage.

LionAGI Native Integration

Builder pattern and Session management for persistent multi-agent coordination

99%+ Reliability

alcall integration with exponential backoff and fuzzy JSON parsing (95% error reduction)

Async-First Architecture

Real-time progress streaming and parallel execution with <1ms tracking overhead

# LionAGI QE Fleet v1.2.0 - Python Implementation
from lionagi import Branch, Session

fleet = {
  "core": [
    "test_generator",      # ReAct reasoning loops
    "test_executor",       # Async parallel execution
    "coverage_analyzer",   # AST-based analysis
    "quality_gate",        # ML validation
    "quality_analyzer",    # Multi-tool integration
    "complexity_analyzer"  # Cyclomatic < 10
  ],
  "performance": [
    "performance_tester",  # Load testing
    "security_scanner"     # SAST/DAST
  ],
  "strategic": [
    "requirements_validator",  # INVEST + BDD
    "production_intelligence", # Incident replay
    "fleet_commander"          # Orchestration
  ],
  "advanced": [
    "regression_risk",     # ML test selection
    "test_data_architect", # Distributed data
    "api_contract",        # Breaking changes
    "flaky_hunter"         # Statistical detection
  ],
  "specialized": [
    "deployment_readiness", # Risk assessment
    "visual_tester",        # UI diff
    "chaos_engineer"        # Fault injection
  ]
}

# Key Metrics: 80% cost savings • 82% coverage
# pytest, Jest, Mocha, Cypress • Python 3.10+
v1.2.0 • Python 3.10+ • LionAGI Framework • MIT License

Sentinel: Multi-Agent Testing

Open-source agentic testing framework built with Rust and Python. Specialized agents working in concert— functional testing, security injection, performance planning—all with explainability first.

PACT-Based Architecture

Proactive, Autonomous, Collaborative, Targeted from the ground up

Explainable Actions

Every agent decision comes with reasoning traces

Human-in-the-Loop

Critical checkpoints, not blind automation

// Sentinel Agent Configuration
{
  "swarm": {
    "agents": [
      {
        "type": "functional-positive",
        "role": "happy_path_validator",
        "autonomy": 0.8
      },
      {
        "type": "functional-negative",
        "role": "chaos_explorer",
        "autonomy": 0.9
      },
      {
        "type": "security-injection",
        "role": "threat_simulator",
        "autonomy": 0.7
      }
    ],
    "orchestration": "hybrid",
    "explainability": "required",
    "human_checkpoints": ["pre-deploy", "security"]
  }
}
v0.1.0-alpha • Coming Q4 2025 • MIT License

Live Experiments

Real-world testing of agentic approaches. Results updated weekly. Failures documented honestly.

In Progress

Agent Reasoning Traces

Can we make every test decision explainable? Building visibility into agent thought processes.

Started: Oct 2025
Planned

Mutation Testing at Scale

Using agent swarms for intelligent mutation testing. Can we beat random mutations?

Starting: Q1 2026
Completed

60% Faster Test Design

AI-assisted test generation at Alchemy. Real metrics, honest challenges, production results.

Completed: Aug 2024

Join the Community

Building the first Agentic Foundation chapter in Serbia and beyond. Monthly meetups, open discussions, and learning together about quality in the age of intelligent agents.

Agentic Foundation Global Meetups

Join the worldwide Agentic QE community. Monthly meetups happening across the globe—from Novi Sad to San Francisco, building the future of quality engineering together.

Global Agentics Foundation

Member of the global Agentics Foundation. Building chapters worldwide, bringing PACT principles and agentic engineering to quality practices across continents.

Explore Agentics.org →

Speaking & Workshops

Looking for a speaker on Agentic QE, PACT principles, or bridging classical to modern quality practices? Let's talk about bringing practical insights to your conference or team.

Get in Touch

Get in Touch

Questions about Agentic QE? Want to discuss consulting or speaking opportunities? Let's connect.

Stay Sharp in the Forge

Weekly insights on Agentic QE, implementation stories, and honest takes on quality in the AI age. No spam, no vendor pitches—just practitioner-to-practitioner learning.

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