How to Determine Which AI Agent Is Actually Working Best for Your Team?

[#1]

Your organization may be using several AI agents.

Developers use one. QA uses another. Support may have its own. Different projects may even be experimenting with different agents for the same type of work.

Then someone asks:

“Which AI agent is actually working best for us?”

Currently fewer teams can answer how many times an agent was used for their Jira issues

Far fewer can answer whether people were actually happy with the result.

That is where AI agent measurement gets interesting.

A natural next question after measuring AI agent activity is: how do you compare agents?

In our earlier article, Beyond Code Generation: How to Measure AI Agent Velocity and ROI in Jira, we looked at measuring agent activity, outcomes, human intervention and quality feedback.

But once you start collecting that data, another question quickly comes up:

“Which AI agent is actually working best for our team?”

And that question cannot be answered from a single month's report.

Don't just measure what the agent did

Imagine an AI agent completed 500 Jira-related tasks last month.

That sounds impressive.

But what if developers had to correct 150 of them?

Now consider another agent that completed only 300 tasks, but the team rated 270 of those executions as useful.

Which one would you choose?

Activity tells you what the agent did.
Feedback tells you what people thought about the outcome.

You need both.

Let the people using the agent become part of the measurement

After an AI agent completes an execution, the person working with the result can provide simple feedback.

For example:

Was the result useful?

👍 Useful
👎 Not useful

This doesn't need to become another complicated process.

A few seconds of feedback, collected consistently, can become extremely valuable over time.

Now the conversation changes for every stakeholder

Different people care about different things.

👨‍💻 Developer

“I want to know which agent actually helps me finish my work faster.”

They can look at executions, outcomes and their own feedback history.

🧪 QA Lead

“Which agent is producing results that my team can actually use?”

They can compare agents based on successful executions and feedback from QA engineers.

👩‍💼 Engineering Manager

“Is AI actually helping my team, and is that improving?”

They can look at team-level trends across months instead of relying on anecdotal feedback.

📊 Product Manager

“Which AI use cases are worth investing in?”

They can compare AI performance across projects and use cases.

🏢 Engineering Leadership

“Where should we standardize or expand AI adoption?”

They can compare teams, projects, agents and historical performance.

The same underlying data answers a different question for each stakeholder.

The important part is the trend

Suppose Agent A receives 82% positive feedback in August.

That is useful.

But what happens if you look at the last six months?

Month	Positive Feedback
March	61%
April	65%
May	69%
June	74%
July	78%
August	82%

Now you have a story.

The agent isn't simply being used.

People are increasingly finding its output useful.

You can also discover the opposite.

An agent may have increasing usage while positive feedback is falling.

That could be a signal that adoption is growing faster than quality.

A one-time dashboard would probably miss that.

Compare agents for the use case that matters

There doesn't have to be one “best AI agent” across the organization.

Agent A might work extremely well for developers investigating Jira issues.

Agent B might perform better for QA.

Agent C might receive the best feedback for documentation.

So instead of asking:

“Which AI agent is best?”

ask:

“Which AI agent is best for this team, project or use case?”

That makes the comparison much more meaningful.

This is where AgentWorkLog fits

AgentWorkLog helps you build a historical view of AI agent executions in Jira and combine that with feedback from the people using those agents.

You can look at performance by:

• AI agent

• Team

• Project

• Use case

• Time period

• User feedback

And then compare it MoM, QoQ or YoY.

So six months from now, you don't have to rely on:

“I think Agent A is better.”

You can look at the data and see:

“Agent A has consistently received higher user feedback across our development projects for the last six months.”

That's a much stronger basis for deciding what to adopt, improve or scale.

AI measurement should become a feedback loop

The goal isn't to create another dashboard that someone checks once and forgets.

It should become a simple cycle:

Agent executes → User evaluates → Data accumulates → Trends emerge → Team improves → Agent strategy evolves

That is how AI measurement becomes useful beyond the initial experiment.

Because ultimately, the best AI agent isn't necessarily the one that completes the most executions.

It is the one that consistently produces outcomes people find useful.

[MeghnaP_LogicLemur Labs]

Curious how others are approaching this.

When your team experiments with multiple AI agents, what do you actually use to decide which one is working better?

Usage? Tokens ? Time saved? Quality? Developer feedback? Rework?

I’m particularly interested in whether teams are tracking this over time rather than evaluating an agent based on a single experiment.

[Joseph K_ Little]

Great point. Usage alone doesn’t show whether an AI agent is actually delivering value. Combining execution data with user feedback and long-term trends gives teams a much clearer way to compare agents by real use case, not just activity.

[MeghnaP_LogicLemur Labs]

Exactly @Joseph K_ Little ! I think the long-term trend is what makes the feedback really useful. A single rating tells us how one execution went, but seeing feedback improve or decline over multiple week / months can reveal whether an agent is actually getting better for a specific team or use case.

[Mia Tamm _Simpleasyty_]

Really interesting approach @MeghnaP_LogicLemur Labs. I particularly like the distinction between measuring how often an agent is used and whether people actually find its output useful.

One thing I’d be curious about is how you account for “silent failure”. A user who gives negative feedback is relatively easy to measure, but sometimes the strongest signal is that someone tries an agent a couple of times, gets poor results, and simply stops using it without leaving any feedback at all.

It feels like combining explicit feedback with behavioural signals — repeat usage, abandonment, task completion, perhaps even how often a result needs to be heavily reworked — could give an even richer picture of which agent is actually delivering value.

And I completely agree with comparing agents by use case rather than looking for a single “best” agent. The winner for documentation may be very different from the winner for support or development.

Great topic — measuring AI quality is becoming almost as interesting as building the agents themselves.

[MeghnaP_LogicLemur Labs]

Absolutely agree @Mia Tamm _Simpleasyty_ . Silent failure is probably one of the most important signals to capture.

Explicit feedback tells us what users say, while behavioural data can tell us what they do afterwards.

With AgentWorkLog, we track the agent activity itself, so even when someone doesn't leave feedback, you can still look at signals such as repeat usage, abandonment and execution history. The activity can also be exported for deeper analysis.

For example, if a user tries an agent several times and then stops using it, that pattern can surface even without a 👍 or 👎.

I think combining explicit feedback + behavioural signals + historical trends gives a much more realistic picture of whether an AI agent is actually delivering value.

And I completely agree that the comparison should be use-case specific. There may never be one “best” agent, but we can identify what works best for a particular team and type of work.