MeghnaP_LogicLemur Labs Atlassian Partner

How does your team currently preserve the rationale behind mid-sprint scope changes or priority shifts without cluttering issue comments?

A question for the Atlassian partner community When a customer asks, "Who changed this, when did it happen, and what was the context?", how does your team investigate today? Do you have a practical way to search historical issue changes, or does the process still involve opening issues individually and reviewing their…

Great topic! Custom field sprawl is often just one part of the broader Jira data governance challenge. For admins looking to get ahead of these issues, QuotaWatch offers a broader approach by bringing Jira data limit monitoring and governance into one place. Beyond custom fields, it helps track multiple configuration…

The 2-Minute Challenge: Test Your Team's AI ROI This Sprint You don't need a multi-month consulting engagement to find out whether AI agents are helping or hurting your velocity. Install AgentWorkLog from the Atlassian Marketplace (30-day free trial). It immediately begins monitoring Rovo and bot assignments across your…

Great roundup. One gap I see in the testing workflow is what happens after a defect/blocker is resolved. Testing tools can help teams find issues, but teams still often rely on manual follow-ups to figure out which Jira issues are now unblocked and who needs to know. We built Unblocked: Blocker Resolved Notifications for…

Curious how other Jira admins troubleshoot this today. When you get a 429 Too Many Requests, do you normally start with application logs, automation logs, or API metrics? The difficult part we've found is not detecting the 429. It's identifying which endpoint and which consumer actually caused it.

I'd be genuinely interested to hear what your 10 issues reveal. Do you usually discover delivery problems because Jira tells you, or because someone on the team finally notices them?

That’s a great example. Let me explain it with a change that has a clear business reason and an audit angle. Imagine a Jira issue is: Priority: Medium Estimate: 5 story points Later, the Product Manager changes it to as there was business pressure to release the feature otherwise there is loss of business opportunity and…

Absolutely agree. Change management and audit trails are important because they tell us what was planned, approved, or recorded. I think there’s another layer that becomes important in highly automated Jira environments: understanding the business context around the change. When a field changes unexpectedly, the question…

I'm curious to hear from partners working with complex JSM environments: What is the most difficult “Why did this change?” problem you've had to troubleshoot for a customer?

Exactly. Less review time only matters if quality holds. AgentWorklog connects human effort with rework, reopens, and defects to show whether agents are actually improving, and suitable for the selected work type.

The shift from experimenting with AI agents to managing them as part of the engineering workflow raises a different set of questions. Which tasks should be delegated? Where does human involvement still add the most value? And how do you know when an agent is genuinely improving over time? I’ve been thinking about this from…

Where can this be useful? 🤖 Changes made by AI agents & automation An AI agent changes priority, assignee, labels or other fields. Later, the team can understand what the agent changed and why behind that. 🚨 Incident & production changes A ticket is escalated, reassigned or reprioritized during an incident. Capture the…

Absolutely @Elena_Elevatic ! Acceptance rate is a critical part of the ROI equation. That’s also where AgentWorkLog can help. It help in capturing human feedback on agent outputs, thus teams can track whether work was accepted, edited, or needed further intervention. So instead of just measuring “How many tasks did the…

@Howard Berkman This is where I think the missing piece is telemetry around the AI work itself. For teams using AI agents inside Jira, you can either download and track audit log periodically and analyse the telemetry or can use the app like AgentWorkLog for Jira which can help make that review process more durable by…

I think this thread also highlights an important distinction: it helps to separate an observed product limitation from a limit that is officially documented as a Jira Cloud data limit or guardrail. As Jira Cloud introduces more documented limits and thresholds, keeping track of the ones that apply to your Jira instance can…

@Audra Bonacki This is not a limit of 50 users in Jira. The new limit is 50 users, space roles, or groups per permission. For example, if a permission currently has: * 20 users * 15 groups * 15 space roles that permission has reached the 50-grant limit. Atlassian is introducing this as part of the new Jira Cloud data…

Monitor your Jira API health before the next 429 Install QuotaWatch for Jira and get visibility into your Jira API rate-limit health, API consumption and rate-limit events. Don't wait for your users to tell you an integration has stopped working.

Exactly. That “did the agent actually land it?” signal is the interesting part. We actually built AgentWorkLog for Jira around this gap. It gives teams a way to see the work AI agents are actually performing in Jira, rather than only looking at the agent response itself. For example, you can start looking at things like: *…

@Sumit Fartode One thing I’d add to this excellent debugging flow is historical visibility. The Debug view is great when you’re investigating one response: which subagent ran, which skills were used, what the request ID was, etc. But once agents are used regularly, another question becomes important: “Is this a one-off…

Really interesting point about making the agent “show its work” @Todd Joyce . I think there’s another layer that becomes important once these multi-agent systems move into production: measuring the work the agents themselves are doing. For example: * How many Jira issues did an agent actually work on? * How long did it…

Here's the part I'm most curious about: If you're a Jira Standard admin, do you currently know: 👉 how close your instance is to the upcoming Jira data limits? 👉 which configuration areas are growing fastest? 👉 whether your Jira health is improving or deteriorating? Or would you only discover it when someone tries to make a…

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…

Great perspective on Rovo governance. I especially liked the point that governance isn't a one-time setup and needs ongoing monitoring. I think there's another piece worth looking at after deployment: how well are AI agents actually performing, and do users find their outcomes useful? That's an area we've been exploring…

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.

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…

Welcome @Vanessa Mouriz !

Thanks @Vijay Shanmugam for your kind words ! 

“Brilliant, high-energy intern” is probably the best description I’ve heard 😄 I’ve seen the same thing with Jira AI. It’s great at reducing the tedious first 80% of the work, but that last 20% often needs someone who understands the context and can validate the outcome. The governance/compliance boundary you mentioned is…

Totally agree. I’ve also found AI much more useful as a thinking partner than as something I can blindly trust to execute a workflow. That “good catch” response after you correct it is painfully familiar 😄 It also got me thinking about how we measure AI value. Usage or number of prompts doesn't tell us much. What really…