
From Model Governance to Machine Agency: Governing AI that Acts, Not Just Answers
Learn how to govern Agentic AI with human oversight, delegated authority, runtime controls, and accountability to enable secure, responsible enterprise AI.
The term “Exploratory Testing” evolved in the past years, software testing experts nowadays discuss the changes in its conception and evolution. James Bach and Michael Bolton described the timeline as a stairway with precise stages: ET 1.0 (Rebellion), ET1.5 (Explication), ET2.0 (Integration) and finally ET3.0 (Normalization).
It’s easy to comprehend the first two steps as a rebellion of the imperative conception of testing and the way through defining its meaning as a separated technique.
Integration leads to a continuum flux between the scripted testing and its exploratory sibling we have been experiencing in the last decade. This continuum fills in the gap, understanding the need for modeling reality in order to describe the behavior of the software and figuring the exploratory elements required to do so. On the other hand, if we are only applying exploratory testing, in our mental models there are many concepts already incorporated, “formalized” by our knowledge and experiences as testers; consciously or unconsciously we “plan” our test strategy.
Also read: PTaaS – Penetration Testing As A Service
The step of Normalization is nothing less than the deprecation of the term “exploratory testing”. There’s no need to use the term “exploratory testing” because the essence of software testing is eminently exploratory and we should conceive activities like test case design, test automation at different levels, verification checklists, among others, as components and tools that assist the tester in the activity.
This leads to an open debate because this new conception of testing takes special significance in the context of agile methodologies. We are glad to hear your thoughts about it ???? Leave us a comment below! How is your conception of ET evolving?

Learn how to govern Agentic AI with human oversight, delegated authority, runtime controls, and accountability to enable secure, responsible enterprise AI.

Swift’s SR 2026 delay changes the ISO 20022 roadmap. Learn what banks need to do now on structured addresses, compliance, data quality and migration.

Code generation got fast. Getting an autonomous workflow into a bank’s production control environment did not. In Banking & Financial Services (BFS), the blocker is rarely the model; it’s trust. A risk officer will not sign off on a system that cannot say why it flagged something, that does its arithmetic inside a language model, or that leaves no audit trail an examiner can open. That is exactly the gap most agentic pilots die in.
Over the last two quarters, we built a portfolio of ten BFS agentic offerings on Alti AIOS™, Altimetrik’s AI Operating System, and deployed them on two stacks: Google Cloud with Gemini and AWS with OpenAI. This piece is the architecture and design principle behind them.
USA (Southfield) - HQ
2000 Town Center, Suite 170
Southfield, MI 48075
+1 248-281-2500