Automated software testing platform provider Tricentis GmbH today launched the company’s new AI Workspace, which orchestrates a team of intelligent agents to help developers and enterprise teams produce high-quality code.

The company’s Agentic Quality Engineering Platform harnesses powerful artificial intelligence agents that help scale software development and quality through autonomous actions while human employees retain oversight, judgement and accountability.

As an overview, Tricentis said AI Workspace operates as a unified command center for a fleet of AI agents with a shared context, providing a place for employees to drive agent-to-agent collaboration and serve as a system of record. It coordinates AI agents for testing, automation, performance and software quality intelligence.

Tricentis argued that right now, generic AI coding tools may appear smart, fast and flashy, but developers risk downtime when they don’t have a complete understanding of an entire project or specific application needs. Such as dependencies, application connections or the full context of enterprise workflows.

“AI is transformative in its ability to create code at unprecedented speed, however the friction caused by lack of confidence in the quality of the output is causing CIOs real pain,” Chief Executive Kevin Thompson said. “While enterprises demand speed, they also can’t afford to introduce risk through insecure or low-quality AI-generated code.”

The AI coding tool speed paradox

Developers are already swimming in a vast sea of potential AI tools offering them a vast array of coding capabilities for code delivery, migration, generation and more.

The entire paradigm of time-to-value has been changing for the development lifecycle within the past 12 months. For many developers it might feel as if it has pulled the rug out from under them trying to understand how these tools help — or ultimately hurt them.

According to a report from METR, when developers use AI tools, they can take up to 19% longer to complete issues, a notable slowdown, although the engineers surveyed believed it sped them up by 20%. Part of this paradox comes from the amount of time spent on debugging AI outputs.

Startups and enterprise companies alike are now trying to close this gap by building better tooling to make AI code and testing better, not just faster, for software engineering workflows.

“We’re already using agentic testing at Tricentis and are experiencing real impact in our transformation projects,” said David Cowell, vice president of AI and machine learning. “A cloud migration that would typically take a few months took us just one week with agentic AI.”

The company said that by employing multiple AI agents, each with its own specialized role and defined responsibilities within the software development lifecycle, it can help retain boundaries and refine AI-generated code.

The new AI Workspace includes Agentic Quality Intelligence that continuously interprets changes, risk and quality signals throughout the lifecycle to determine release readiness, automatically directing testing and escalating to humans only when judgment is needed. This reduces false positives and alert overload.

Test automation builds the next generation of productivity by intelligently reusing test modules to reduce duplication, enhance maintenance and manage risk. Agentic performance testing helps provide enterprise-ready AI-driven performance validation by analyzing the design and execution of code, including application programming interfaces and systems.

Part of the company’s qTest product, Agentic Test Creation works with test engineers to help them author in-context tests. It allows them to write natural-language tests, providing them with reusable test cases that work faster and more consistently and reduce duplication and reliance on specialized expertise.

Image: SiliconANGLE/Microsoft Designer