TestTalks.AI - The Testing AI Podcast

Test-Talks.com welcomes Adam Sandman who has been working in the IT industry for the past 25 years in areas such as architecture, agile development, testing and project management. Currently Adam is the Founder and CEO of Inflectra Corporation, where he is interested in technology, business and enabling people to follow their passions. At Inflectra, Adam has been responsible for researching the tools, technologies and processes in the software testing and quality assurance space. 
In this episode will be a world exclusive of SureLine.AI Go From Not Worrying About Your AI Agents to Knowing They are Safe, Reliable, and Effective:
Ask SureLine™ to test your agent, like you were talking to an experienced test manager on your team. SureLine™ will designand implement dynamic tests, using collections of bespoke testing agents. It provides a report of the AI under test, highlighting strengths and weaknesses, risks, quality concerns, and more.SureLine™ is a whole new category of QA tool. Testing AI is not like normal testing. You can’t rely on traditional testing toreally know the state of your AI. You need a quality assurance tool built from the ground up, that leverages AI to dynamicallyprobe and test AI. You need SureLine™.

Test-Talks.com welcomes Paul Gerrard legendary consultant, teacher, author, programmer, tester, conference speaker, rowing coach and publisher. He has conducted consulting assignments in all aspects of software testing and quality assurance, specializing in test assurance. He has presented keynote talks and tutorials at testing conferences across Europe, the USA, Australia, South Africa and won the most amount of lifetime achievements awards.
In 2026, Context Engineering will be king, in this episode you will better understand DEFOSPAM an Vibe Requirements engineering approach to AI-in-the-loop (AIitL) to help create better requirements or stories, building on the success of the Business Story Method (published back in 2012) how can you utilize AI as a coach / mentor to help improve business requirements in 2026?
#AIAssurance #automationtesting #testautomation #automationtesters #softwaretesting #functionalautomation #nonfunctionalautomation #testingcareer #e2eautomation #ai #aiintesting #autoops #aiaugmentedtesting #generativeai #AIAugmentedTesting #eggplantGAI #generativeAI 

After hearing benefits Generative AI will bring to testing over the last few years, the AI paradigm is evolving to orchestrate complex multi-step processes within automation. The recent introduction of Agentic AI enables the design, thinking, and learning of large-scale, interconnected tasks that require adaptive decision-making and real-time adjustments, enhancing the capabilities of automation workflows. At the same time, organizations are grappling with the operational demands of legacy systems. The shift to dynamic runtime test automation, powered by next-generation technologies such as Large Action Models (xLAM) and Visualization of Thought (UFO), presents both opportunities and challenges. How can quality engineers strike the right balance between adopting cutting-edge solutions and maintaining enterprise-scale automation?In this keynote, Jonathon will explore how test automation can thrive amidst these dual forces. Drawing on real-world R&D and practical implementations, we will delve into:– AI-Augmented Testing with Agentic AI: Harnessing Agentic AI to enable adaptive, autonomous decision-making within test automation workflows. Learn how Agentic AI-driven systems can dynamically identify, prioritize, and execute test cases, reducing manual intervention and enhancing testing efficiency.– Successes, Failures, and Lessons Learned: A candid look at organizations navigating the integration of Agentic AI and other emerging technologies, with real-world case studies showcasing the journey from experimentation to impactful results.– Sustainable Automation: Balancing speed, scalability, and maintainability to future-proof your automation investments. Explore how Agentic AI fosters sustainability by continuously optimizing resources and refining testing processes in real time.

As artificial intelligence moves beyond traditional automation and generative capabilities, ensuring AI’s reliability, transparency, and ethical integrity becomes a pressing challenge. This session introduces AI-Assurance, a new paradigm for validating and governing Agentic AI systems, which leverage Computer Use Automation (CUA), Chain of Thought (CoT), and GraphRAG architectures to enhance model-driven decision-making, self-learning, and adaptive agent-based CUA execution. Building on decades of advancements in cognitive engineering, AI and automation,, this talk explores how large action models (LAM), large vision models (LLaVA) and AI-driven software agents are redefining productivity & assurance across the software development lifecycle (SDLC).

In this video, we're diving into Testers.AI's Model Context Protocol (MCP) Cursor Integration. Learn all about MCP and how it can enhance your testing process! #MCP #testing #AI

Dedicated to Tariq King the King of AI 
As artificial intelligence moves beyond traditional automation and generative capabilities, ensuring AI’s reliability, transparency, and ethical integrity becomes a pressing challenge. This session introduces AI-Assurance, a new paradigm for validating and governing Agentic AI systems, which leverage Computer Use Automation (CUA), Chain of Thought (CoT), and GraphRAG architectures to enhance model-driven decision-making, self-learning, and adaptive agent-based CUA execution.
Building on decades of advancements in cognitive engineering, AI and automation,, this talk explores how large action models (LAM), large vision models (LLaVA) and AI-driven software agents are redefining productivity & assurance across the software development lifecycle (SDLC).
Attendees will gain insights into the evolution of building AI-infused system methodologies, the shift from traditional process automation to autonomous AI agents, and the role of governance models like ISO 42001 and AI risk compliance frameworks (AI TRiSM / ISO / NIST).
Key topics include:
Agentic AI and the Future of AI-Augmented Workflows – How AI systems move from passive assistance to active moral decision-making.
GraphRAG and Chain of Thought Reasoning – How structured AI reasoning enhances software quality, transparency, and compliance.
CUA and Large Action Models – The emergence of autonomous, neuro-symbolic, AI agents for real-time computer use execution.
AI-Assurance in the Age of Regulation – Navigating compliance, ethical AI governance, and risk management frameworks.
This session is essential for AI practitioners, modern software development, and business decision-makers seeking to integrate AI-Assurance governance into their AI-productivity or AI-workforce workflows while avoiding bans from emerging AI liability laws and EU AI Act compliance mandates.

As artificial intelligence moves beyond traditional automation and generative capabilities, ensuring AI’s reliability, transparency, and ethical integrity becomes a pressing challenge. This session introduces AI-Assurance, a new paradigm for validating and governing Agentic AI systems, which leverage Computer Use Automation (CUA), Chain of Thought (CoT), and GraphRAG architectures to enhance model-driven decision-making, self-learning, and adaptive agent-based CUA execution.Building on decades of advancements in cognitive engineering, AI and automation,, this talk explores how large action models (LAM), large vision models (LLaVA) and AI-driven software agents are redefining productivity & assurance across the software development lifecycle (SDLC).Attendees will gain insights into the evolution of building AI-infused system methodologies, the shift from traditional process automation to autonomous AI agents, and the role of governance models like ISO 42001 and AI risk compliance frameworks (AI TRiSM / ISO / NIST).Key topics include:Agentic AI and the Future of AI-Augmented Workflows – How AI systems move from passive assistance to active moral decision-making.GraphRAG and Chain of Thought Reasoning – How structured AI reasoning enhances software quality, transparency, and compliance.CUA and Large Action Models – The emergence of autonomous, neuro-symbolic, AI agents for real-time computer use execution.AI-Assurance in the Age of Regulation – Navigating compliance, ethical AI governance, and risk management frameworks.This session is essential for AI practitioners, modern software development, and business decision-makers seeking to integrate AI-Assurance governance into their AI-productivity or AI-workforce workflows while avoiding bans from emerging AI liability laws and EU AI Act compliance mandates.