Tricentis Preps Wave of Additional AI Testing Capabilities

Tricentis is providing early access to multiple artificial intelligence (AI) capabilities that it is gearing up to roll out later this year via a Tricentis Transform initiative, including an autonomous AI agent, dubbed Aida, that explores web and Windows desktop applications to surface defects, weaknesses and other pot…

Tricentis is providing early access to multiple artificial intelligence (AI) capabilities that it is gearing up to roll out later this year via a Tricentis Transform initiative, including an autonomous AI agent, dubbed Aida, that explores web and Windows desktop applications to surface defects, weaknesses and other pot…

In context

• Topic: Inteligencia Artificial — IA aplicada al negocio: casos, límites, costos y gobierno.
• Source: DevOps.com
• Published: 26/08/2026

Continue reading at the original source →

Excerpt published automatically by the site radar. The full text belongs to its publisher and is linked above.

Why it matters

The conversation about artificial intelligence has become noisy, and that makes it harder to see what matters. What matters is rarely the model. It is which concrete process ends up cheaper, faster or more reliable, and who is accountable when the answer comes out wrong.

The first thing I look at is not model capability but the data feeding it. Almost every project I have seen fail did not fail on the algorithm. It failed because the data lived in three systems with three different definitions and nobody wanted to be the one to fix that.

What usually goes wrong

The mistake I have seen repeat most is measuring nothing before starting. It gets deployed, everybody feels things improved, and when somebody asks by how much, there is no answer. Without a baseline there is no way to defend next year's budget, and that is where good projects die.

What to watch

• Who reviews the model outputs and how often — the point at which AI becomes auditable.
• Cost per query at real volume, not pilot volume: the variable that produces the most surprises.
• Where the data ends up and under what contract, especially when customer information is involved.

How I read this entry

My practical advice is to fix on day one what happens when the model is wrong: who reviews, how often, and at what point it gets switched off. It sounds defensive, but it is exactly what lets you be aggressive later, because the risk stopped being an unknown.

This entry is an excerpt from the original source, selected by the site radar. The commentary above is the site's own and does not belong to the cited publisher.