Automation Testing Framework for Reliable Autonomous Agentic AI

With the rise of autonomous systems (AS) and agentic artificial intelligence (AI), a heightened automation of testing processes is required to build, deploy, or repair reliable intelligent systems. For this purpose, we developed a framework allowing any intelligent tester, whatever human or agent, to automate test scripts on their developed applications and provide detailed results of the users’ automated test cases execution. This framework enables testers to create and replay automated test scripts quickly, so automation can be used in short development projects by unskilled (manual or agentic) testers. Furthermore, the implementation of this framework aims to solve the problem of performing secure regression testing on applications before each new application release candidate and every time an intelligent developer - human or artificial - makes changes to the programmes code, which can take up huge amounts of testing time in a software development project and over the life cycle of the product application. Thus, this automated testing framework reduces the time to carry out testing so the costs, while it increases quality testing and system reliability. The first prototype of this framework was developed successfully and tested thoroughly, and its deployment in real-world context showed promising results, paving the way for reliable agentic AI systems.

Authors

Institutions

Publication Details

Published
2025-05-29
DOI
https://doi.org/10.1109/eras63351.2025.11135911
Primary Topic
Fault Detection and Control Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Automation Testing Framework for Reliable Autonomous Agentic AI

Joanna Isabelle Olszewska, Grant Martin
Fault Detection and Control Systems
article

Automation Testing Framework for Reliable Autonomous Agentic AI

Joanna Isabelle Olszewska, Grant Martin
article en

Abstract

With the rise of autonomous systems (AS) and agentic artificial intelligence (AI), a heightened automation of testing processes is required to build, deploy, or repair reliable intelligent systems. For this purpose, we developed a framework allowing any intelligent tester, whatever human or agent, to automate test scripts on their developed applications and provide detailed results of the users’ automated test cases execution. This framework enables testers to create and replay automated test scripts quickly, so automation can be used in short development projects by unskilled (manual or agentic) testers. Furthermore, the implementation of this framework aims to solve the problem of performing secure regression testing on applications before each new application release candidate and every time an intelligent developer - human or artificial - makes changes to the programmes code, which can take up huge amounts of testing time in a software development project and over the life cycle of the product application. Thus, this automated testing framework reduces the time to carry out testing so the costs, while it increases quality testing and system reliability. The first prototype of this framework was developed successfully and tested thoroughly, and its deployment in real-world context showed promising results, paving the way for reliable agentic AI systems.

University of the West of England (GB), University of the West of Scotland (GB)
Openalex Percentile: Top 73%
Fault Detection and Control Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Automation Testing Framework for Reliable Autonomous Agentic AI — Joanna Isabelle Olszewska, Grant Martin · (2025) | TGRS Research Map | TGRS