Every week, another AI agent joins our Jira projects.
Atlassian Rovo.
Claude.
GitHub Copilot.
Gemini.
Internal automation agents.
They create issues, analyze tickets, suggest fixes, write comments, and even complete work.
But here's a question many engineering leaders are starting to ask:
If AI is becoming part of our engineering workforce, how do we actually measure its contribution?
Not prompts.
Not tokens.
Not licenses.
Actual work.
The invisible workforce inside Jira
Imagine this conversation during a quarterly business review.
CFO
"We're spending thousands on AI subscriptions."
VP Engineering
"Yes."
CFO
"Great. How many engineering hours did we save?"
...
Silence.
Because today, Jira can tell us:
- who completed an issue
- how many story points were delivered
- sprint velocity
- cycle time
But it cannot answer questions like:
- Which AI agent worked on this issue?
- How long did it work?
- Which agent saves us the most time?
- Which agent constantly needs human intervention?
- Which AI investment is actually paying off?
As organizations adopt more autonomous agents, these questions become increasingly important.
Introducing AgentWorkLog for Jira
Today we're excited to launch AgentWorkLog, a Forge app built specifically for Jira Cloud that helps organizations understand, measure, and govern AI agents working inside Jira.
Instead of asking developers to manually log AI activity, AgentWorkLog automatically tracks the lifecycle of an agent session from start to finish.
No workflow changes.
No additional fields.
No manual worklogs.
Just visibility.
A real-world example
Consider a platform engineering team using Rovo to triage production bugs.
Every morning:
- Rovo is assigned new incidents.
- It analyzes logs.
- It suggests root causes.
- It adds investigation comments.
- Developers review the recommendations.
After a month, management asks a simple question:
"Was Rovo worth the investment?"
Without telemetry, the answers are usually opinions.
"It feels useful."
"Developers seem happier."
"I think it saved time."
None of these help justify AI spending.
With AgentWorkLog, the discussion changes.
Instead of opinions, engineering leaders can see:
- Total issues touched by the agent
- Logged execution hours
- Sessions completed
- Human feedback
- Good vs Needs Rework ratio
Now the conversation becomes data driven.
Why this matters
Most organizations are entering an era where humans and AI collaborate on the same backlog.
That creates an entirely new governance challenge.
You already measure developers.
Soon you'll need to measure AI workers too.
Not to replace humans.
But to answer questions such as:
- Which agent delivers the best results?
- Which prompts generate poor outcomes?
- Which teams benefit most from AI?
- Which licenses should be renewed?
- Where is human intervention still required?
Without these answers, AI becomes another expense rather than a measurable investment.
Built for enterprise teams
AgentWorkLog was designed with enterprise governance in mind.
It automatically:
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Tracks agent session duration
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Detects human takeovers
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Captures one-click ๐ / ๐ feedback on each Agent run
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Provides project and organization-wide analytics
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Exports audit-ready CSV reports
Because it's built entirely on Atlassian Forge, it also follows a security-first approach:
- Zero external data egress
- Only three Jira scopes
- No issue content stored
- No workflow modifications required
Who benefits and how ?
CTOs & VP Engineering
Understand whether AI investments are delivering measurable value.
Engineering Managers
Compare agent quality and identify where human rework is increasing.
Scrum Masters
Separate machine execution from human effort for better sprint planning.
Jira Administrators
Deploy without changing existing workflows.
Security & Compliance Teams
Maintain an auditable record of AI activity while keeping all data inside Atlassian.
The beginning of AI workforce governance
For years, we've measured human productivity.
The next challenge isn't replacing developers.
It's understanding how humans and AI work together.
The organizations that can measure AI contribution will make better investment decisions, improve prompt quality, and build trust in autonomous agents.
We believe that visibility starts with telemetry.
That's exactly why we built AgentWorkLog: AI Agent ROI, Time Tracker & Worklog for Jira.
We'd love your feedback
We're excited to bring AgentWorkLog to the Atlassian Marketplace and would love to hear how your team is adopting AI inside Jira.
