Do Rovo tools save time, credits, or both?

Iryna Komarnitska_SaaSJet_

While developing Rovo tools for our Jira apps, we became curious about what happens when an agent has access to those tools. Does it answer faster? Use fewer credits? How much difference does the reasoning tier make?

So we ran a small comparison on a Jira test site, asking workflow questions with and without specialized reporting tools. The scenarios covered status duration, status entry dates, assignee time, and transitions.

One finding stood out straight away — faster answers didn’t always mean fewer credits.

What the test found

For single-item questions, the results were averaged across four scenarios.

Setup	Runs	Mean credits per answer	Mean response time
Basic Rovo · Quick	12	10	48 sec
Agent with reporting tools · Quick	12	10	20 sec
Basic Rovo · Premium	12	53.4	267 sec
Agent with reporting tools · Premium	4	10	26 sec

In Quick, the tool-equipped setup took about 58% less time, while the credit reading stayed at 10.

In Premium, both response time and credit usage decreased. However, the tool-equipped agent had fewer runs, so that comparison needs more testing.

We measured response time from sending the question to receiving the finished answer, and credits through changes in the organization’s usage meter.

A few limits matter here. This was a small test on one Jira site comparing complete agent configurations. It doesn’t isolate the effect of tools alone. We also don’t have token counts, tool-call traces, or scored answer accuracy in these results.

What should count as a better result?

The Quick results raise a practical question. If an agent answers faster but consumes the same credits, how should a team value that improvement?

For a recurring task, waiting time matters. So does the effort needed to check the answer. An agent that responds in 20 seconds but needs three follow-up questions may leave you worse off than one that takes a minute and gets the full task right. It has mostly demonstrated enthusiasm. 🙂

That makes three measurements worth tracking together.

• Answer quality — does the result match a trusted source and cover the requested scope?
• Time to a usable answer — including clarification and corrections.
• Credits for the complete task — including retries and follow-up messages.

Tools can change the work an agent performs, but the billing effect depends on the task and configuration. Atlassian describes both fixed and variable credit usage, with reasoning effort, search volume, tool use, and model choice among the factors affecting variable usage.

»»How Rovo credits work

For the next round of testing, repeated runs and answer-quality checks would help establish whether the faster responses consistently deliver everything the task requires.

What are you seeing in practice?

Have you compared the same Rovo task with different tools or reasoning tiers?

Was the biggest difference speed, credit usage, or how much checking you needed afterward? And how are you deciding when an agent is ready for everyday use?

Dario Guera

Thanks for sharing the numbers so openly, especially the limits of the test. The Quick result is the most useful warning here: a faster answer is not automatically a cheaper one, so time and credits really do need to be tracked separately.

For a next round, I would suggest keeping the comparison as like-for-like as possible: the same set of questions, the same number of runs in each setup, and the same definition of a finished answer. It is also worth recording the total credits for the whole task, including any retries or follow-up questions, rather than only the credits for a single successful answer.

Reporting a median alongside the mean would help too, since one very slow or credit-heavy run can move an average a lot when the sample is small. Paired with a simple quality check, such as whether the answer matched a trusted source without extra correction, that would make it much easier to judge when a setup is genuinely ready for everyday use.