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Dragan Spiridonov

Dragan Spiridonov profile image
Dragan Spiridonov
Head of Agentic Quality Engineering @ Cognitum.One | Ambassador, Agentics Foundation

In IT since 1996, establishing and leading QA/QE functions since 2014.
8 years at Alchemy, building QA/QE from scratch.
Since October 2025, the founder of Quantum QE, Ambassador for Agentics Foundation Serbian chapter, and creator of the Agentic QE framework and Agentic QE Fleet, an open-source AI-powered QA/QE platform.

🎂 MoTaBirthday | April 19, 2015
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Chapter Lead
Prompting is so yesterday...hello Loop Engineering image
Loop engineering is moving from prompting agents and towards designing the systems that prompt.And for what it's worth, no, I am not looping yet, I'm still happy prompting away.However, Addyo Osma...
11 Jun
Following the trend....Unhinged Bio image
Following the trend from Rosie and Dragan I tried the prompt too 😄The prompt: Hey ChatGPT, write a totally bonkers, unhinged bio about me (Parveen Khan) in 200 words.Then, create an image.
19 May
Unhinged Bio - Rahul Parwal image
 The prompt: Hey ChatGPT, write a totally bonkers, unhinged bio about me (Rahul Parwal) in 200 words. Then, create an image.Inspired by Dragan Spiridonov and rosie sherry 🐌 unhinged bio moment...
17 May
Rosie's unhinged LLM bio — Brighton's most dangerous community architect (!) image
Following Dragan's unhinged LLM bio post. This is mine.The prompt: Hey Claude/Gemini/etc, write an unhinged bio about me (Rosie Sherry) in 200 words, totally bonkers. ClaudeProduced the image attac...
17 May
From ad-hoc prompting to Agentic Engineering: Latest trends in AI image
Learn how software engineering is shifting from basic prompt engineering to autonomous AI agents and intent-based workflows.
16 Jul
Agentic QE: From AI Assistant to AI Workforce image
A paradigm shift from using AI as a coding assistant to directing AI agents as an intelligent workforce
20 Mar
Harness Engineering Harness engineering is an emerging AI methodology focused on creating reliable, structured environments ("harnesses") that enable AI agents to function securely and effectively in production. It involves designing feedback loops, constraints, and validation systems rather than just relying on model improvements.  Core Principles of Harness Engineering (as of 2026) Agent Control & Reliability: Moving from "model-first" to "harness-first" by building scaffolding that allows agents to work on complex tasks for hours or days. Mechanical Enforcement: Translating documentation into hard code constraints (guardrails) to ensure compliance, rather than relying on manual, human-driven review. Context Engineering: Curating the knowledge base and designing the codebase for agent legibility, ensuring agents know what to do and how to do it. Feedback Loops: Implementing automated systems that verify agent output, correct mistakes, and manage multi-agent workflows across repositories.  Key Components in a Harness Grounding: Ensuring the agent knows its position, constraints, and the current state of the project. Architecture & Design: Structured documentation (e.g., AGENT.md, PLANS.md) and directory structures designed for AI, not just humans. Evaluation: Using tools to continuously test the agent’s work (e.g., using Playwright for browser automation).  Industry Applications AI Agent Development: Used by organizations like OpenAI and Anthropic to make AI coding agents reliable. CI/CD Optimization: Integrating agent workflows into CI/CD pipelines to manage deployment and security. QA and Testing: Automating the software development lifecycle from building to deployment.
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