Tag: AI agents

One question kept coming up in Amsterdam 

The licences are bought, the data has moved, and people have logged in. So why does the business case still feel like a promise? We heard some version of that question at the Cprime booth during Atlassian Team ’26 Europe (6 to 8 October 2026, RAI Amsterdam). For years, the hard part was the Atlassian Cloud migration. Now leaders want to know when the return shows up. 

Key takeaways from Atlassian Team ’26 Europe 

• Rovo AI: start with two or three repetitive tasks, such as the weekly status report, and track the hours saved before scaling. 

• Ways of working: treat the Atlassian Cloud migration as day one. Simplify workflows, agree what “done” means, and give leaders one dashboard they trust. 

• Usage-based pricing: audit licences and apps, remove dormant accounts and duplicate apps, and link remaining spend to outcomes. 

• Apps and Data Center: Atlassian Cloud now handles much of what custom apps did. Rebuild only the logic that sets your business apart on Atlassian Forge. 

• Jira Service Management: connect JSM to Jira and Confluence early so service, engineering, and knowledge work from the same picture. 

From migration to momentum 

The leaders we met at the event have already moved to Atlassian Cloud and are curious to know what comes next. Our answer is simple: tools alone don’t change outcomes. The platform creates the opportunity, and the value comes from how teams plan, collaborate, and deliver on it every day. When ways of working evolve alongside the technology, the return follows. 

Five themes stood out. Each one shows where that shift is already happening, and where the biggest opportunities are waiting. 

1. From access to adoption: making Rovo AI count 

Many teams already have Rovo AI (Atlassian’s AI for search, chat, and agents), and the leaders we met are eager to make the most of it. Their questions were practical. What should we automate first? Which Rovo use cases are worth the effort, and how do we prioritise? How do we show the business it’s paying off? 

We’ve found that teams who see results start small and stay focused. Rolling Rovo out to everyone at once makes it hard to build new habits or measure impact. A few targeted use cases build momentum quickly. 

Where to start: pick two or three time-consuming, repetitive tasks. The weekly status report is a great example. Someone in the PMO often spends half a morning pulling updates from Jira and Confluence. A Rovo agent can draft it in minutes, and the project lead reviews it and hits send. Track the hours saved for a month, share the results, then take on the next task. 

2. Atlassian Cloud has changed the platform, but not the way of working 

Most leaders we spoke to moved to Cloud for faster releases, better visibility, and less overhead. A lot of them haven’t seen those yet. It’s a familiar pattern. A company lifts dozens of Jira projects into Cloud, complete with the custom fields, workflows, and five-step approvals someone built ten years ago. The clutter comes along for the ride, so reports stay slow and planning still lives in spreadsheets. 

What helps: treat migration as day one, because the value comes from what you change after go-live. Strip back the workflows, agree on what “done” means, and give leaders one dashboard they trust.  

The same thinking applies at portfolio level. With its Strategy Collection, Atlassian is moving beyond work management by connecting strategy and investment to delivery. Its portfolio management capabilities are becoming a real alternative to traditional platforms. At Cprime, we’re exploring this with customers now and working closely with Atlassian’s Strategy Collection team as the offering evolves. 

Underneath it all sits the Teamwork Graph, the connected layer that links goals, people, knowledge, and work across Atlassian. The intelligence is already there. The opportunity is to use it better by keeping strategy, knowledge, and execution aligned. Our System of Work Accelerator, which we demonstrated to customers in Amsterdam, is a practical place to start. It shows how you’re using your Teamwork Graph today and where to strengthen your data and connections to support Rovo and your strategic goals. Pair that with the right skills and habits, and leaders can see how work contributes to their objectives and show what AI is really delivering.  

3. Atlassian usage-based pricing puts every licence in the spotlight 

The shift to Atlassian usage-based pricing has put cost firmly on the agenda. When you pay for what you use, every app and automation needs a reason to exist. Finance spots the gaps fast, and “Are we getting value?” stops being a feeling and becomes a number someone has to stand behind. 

A practical first step: audit what you’re paying for, check who actually uses it, and cut what nobody needs. Dormant accounts and two apps doing the same job are the usual quick wins. Then link what’s left to the outcomes it supports, so your next budget meeting starts with evidence. Our System of Work Accelerator FAQs go deeper. 

4. Moving from Data Center to Cloud: your apps may not be the blocker you think they are 

For teams still on Data Center, the biggest worry about moving to Cloud is their apps. Years of ScriptRunner scripts and custom apps now support critical processes, and rebuilding them feels risky, expensive, and disruptive. The good news is that Atlassian Cloud now handles much of that work out of the box. The custom logic that truly sets your business apart can be rebuilt on Atlassian Forge, Atlassian’s cloud app development platform, allowing you to keep what matters, simplify what doesn’t, and open the door to Cloud capabilities like Rovo along the way. 

Before you decide: take a fresh look at your apps. Our Cloud Migration Blueprint maps what can move as is, what Cloud already handles, and what’s worth rebuilding, so you can reduce your migration scope and your licence bill in one go. 

5. Beyond the help desk: Jira Service Management steps up for IT service 

Interest in Jira Service Management (JSM) went far past basic ticketing. Leaders want one connected IT service management (ITSM) platform for incidents, changes, problems, and assets, so their teams can resolve issues faster and see what’s really driving them. 

Picture a bank’s mobile app going down. The incident in JSM links to the Jira ticket for the fix, and the fix links to the Confluence page that explains what happened. Everyone works from the same picture, and Rovo can surface answers faster because the knowledge is already connected. 

Make it count: connect JSM to the rest of your Atlassian tools early. The sooner service, engineering, and knowledge sit together, the faster your teams can move from firefighting to improving. We’ve seen what this makes possible. For one Fortune 50 technology company, we built a JSM solution that cut average handling time by 15 minutes per issue and is forecast to save millions of dollars a year. 

A theme to watch: developer experience 

Developer experience came up again and again at the event. Engineering leaders want a clearer view of how their teams deliver, and whether their growing AI investment is paying off. It’s the same question behind all five themes: are your tools changing how work actually gets done? For product and engineering teams, Rovo-augmented product development is how we help answer it, by bringing AI into planning, building, and shipping so leaders can see the impact. 

How Cprime turns Atlassian investment into impact 

Our insights come from more than 20 years of helping organisations change how they work. When PayPal moved to Atlassian Cloud, we helped rework its workflows and improve the developer experience for more than 35,000 active users, as part of the largest Atlassian Cloud migration in history. It’s also why Atlassian named us its Partner of the Year for Cloud Transformation Services. 

If you want your Atlassian investment to pay back, here’s where we’d put your energy: 

• Changing how teams work: Treat Cloud as an operating model shift and plan for it that way. Plenty of partners can move your data, but the bigger gains come from rethinking how teams plan, track, and deliver once they’re there. That’s why large organisations often migrate with one partner, then bring Cprime in for this part. 

• Adoption that sticks: Put as much effort into your people as into the platform. Atlassian has made adoption a top priority, and it should be yours too. Rovo and Cloud only pay off when teams know how to use what they already have, so start with a few proven use cases, train the people who’ll use them, and keep building new habits around them. 

• Going deep where it counts: Resist the urge to fix everything at once. Pick the few areas where value is leaking most and get those right first. For the leaders we met, that meant getting more value from Atlassian Cloud, driving Rovo AI adoption, or expanding JSM across IT service. It’s the same focus we apply to our own work. 

Picture the quarterly investment review. The portfolio report shows one number. A business leader quotes another from a spreadsheet. Everyone turns to the one administrator who knows how the report is built. 

If that sounds familiar, your platform may have drifted. Drift is common in Targetprocess environments because the platform is so flexible. It adapts to how each team and portfolio work. But without a shared operating model to guide that flexibility, small and reasonable changes add up, and the leaders accountable for investment decisions end up paying for it. 

To help Apptio Targetprocess customers get more value from their platform, Cprime hosted an IBM Targetprocess webinar led by Solutions Advisor Carollyn Gehrke. Drawing on nearly 30 years in technology business management (TBM), portfolio, and product management, she explained how drift happens and how to reverse it. 

How platforms drift 

Carollyn compares a Targetprocess environment to a growing city. In her analogy, roads are the workflows and fields that move work through the system. Neighborhoods are the teams and portfolios that use it. Street signs are the views and reports people rely on to find their way. Detours are the exceptions added to meet an urgent need. 

No city decides to become hard to navigate. It happens one road at a time, until residents know where they want to go but not how to get there. Targetprocess grows the same way. An urgent report gets a new field, one team gets its own workflow, and one leader gets a custom view. Over time, the configuration reflects years of requests more than the strategy it was built to serve. 

When people get lost, it is easy to blame the tool. However, the real causes are usually unclear ownership, inconsistent processes, undocumented decisions, and unmanaged change. 

Targetprocess may be where the problem becomes visible. It is rarely where the problem begins. 

Why one fix is never enough 

Three problems drain value from portfolio platforms, and each one fuels the next. 

1. Complexity comes first. Flexibility turns into risk when fields, workflows, and reports stop working as one system. 

2. Adoption follows. If leaders quote numbers the system does not show, the platform is being worked around. 

3. Key person dependency completes the set. The environment becomes fragile when one person holds every shortcut and hidden connection. 

For TBM leaders, the impact is concrete. When the data behind funding decisions is uncertain, every investment call carries more risk. 

If your Targetprocess expert left tomorrow, what would be at risk? 

Most Targetprocess environments have one person who knows every shortcut, exception, and workaround. Four questions show how exposed you are. Who understands why the configuration works the way it does? Where are decisions and exceptions recorded? Who can fix a broken report or integration? How quickly could someone else take over? 

If every answer points to the same name, you have a key person dependency. These experts are extremely valuable, so the goal is to turn what they know into something the whole organization owns. 

Treat the platform as a product 

TBM leaders already manage technology spend as a portfolio. The platform behind enterprise agile planning deserves the same discipline. 

Start with strategy, then decide what to fund, change, continue, and stop. Design the data, workflows, and views those decisions need, plan for adoption, and measure whether the investment delivers. Most organizations start by asking what the tool can do. A better first question is which decisions the business needs to make. 

Targetprocess best practices 

You do not need to start over. A five-step framework fixes what you already have. 

• Stabilize by finding the problems that matter most. 

• Standardize how changes are approved, documented, and managed. 

• Enable people with documentation and training they can actually use. 

• Align platform priorities with your portfolio strategy. 

• Sustain progress by tracking usage, technical health, and integration reliability. 

To make the gains last, document the intent behind each configuration decision and design views around the decisions people actually make. 

Start small. In the first 30 days, run a Targetprocess configuration health check to find your biggest risks, dependencies, and most valuable use cases. Over the next quarter, set clear ownership, governance, and support. After that, share the platform roadmap openly and keep improving with intent. 

The cost of waiting 

Invest 10 to 15 percent annually in user adoption and understanding. It may sound high. But count what complexity already costs. Leaders stop trusting reports. Administrators lose weeks to exceptions. Teams build workarounds. The organization changes while the system stands still. 

Complexity carries a cost. It just never appears on the budget. 

Stabilizing the platform protects what you have already invested. Leaders get numbers they can trust, knowledge stays inside the organization, and more people use the system. 

Building a platform leaders trust 

Picture the end state. Configuration is simple. Decision rights are clear. Leaders trust the data enough to fund from it. No single person holds all the keys. 

Getting there starts with a new question: how will the platform keep supporting growth, governance, and investment decisions as the organization changes? 

When ownership is clear and every change connects to strategy, Targetprocess becomes a trusted view of where the organization stands and where its investments should go. 

Watch the on-demand webinar for practical steps to simplify your Targetprocess environment and strengthen adoption. 

Many IT leaders believe the right platform will fix their service desk. Swap the old ticket log for a modern suite, and efficiency should follow. 

It won’t. Software is an amplifier. Digitize a chaotic process, and you get automated chaos. ITIL practices give the process its shape before the platform scales it. 

Cprime sees this pattern often in service management engagements. In a recent onboarding review, the lead of a specialized healthcare IT team described the starting point plainly: 

“The team’s processes are very far from being matured. There is very little documentation… A lot of the ITIL stuff they hadn’t heard of ITIL until I turned up.” 

Much of the team’s knowledge lived in people’s heads rather than in documented processes. That detail separates a high-return transformation from an expensive one. Improving service management is more than a technology project. It is a chance to establish a better way of working. 

What happens when ITSM processes aren’t mature? 

A new service desk can expose process gaps, but it won’t resolve them on its own. The same is true of an existing platform carrying years of configuration and workarounds. 

When teams lack consistent ITIL-aligned practices, three problems tend to emerge: 

1. Tribal knowledge replaces standard workflows. Issues get resolved based on who answers the phone, rather than a defined incident management practice. 

2. Root causes stay hidden. Without formal problem management, teams spend most of their bandwidth fighting the same fires. 

3. Process feels like an uphill struggle. As the team lead noted, introducing service management into an unstandardized environment is hard. Resistance is high when process has never been taught. 

The result isn’t necessarily a bad service desk. It’s a service management operation without a consistent operating model behind it. 

Jira Service Management is a force multiplier for good process 

Jira Service Management gives organizations a flexible platform that connects service teams, development, operations, and the wider business. Flexibility, however, is different from process maturity. 

If workflows, ownership, escalation paths, and service definitions are unclear, configuration alone won’t solve the problem. Think of it this way: 

• Immature processes + modern technology = faster execution of inconsistent processes 

• Defined ITSM practices + modern technology = scalable service management 

The technology matters. What you choose to standardize, automate, measure, and improve matters just as much. 

Start with ITIL practices, then configure the platform 

The most important work in a JSM implementation happens before configuration begins. The same principle applies when you improve an existing service management environment. 

1. Define how work should flow 

Start with the core ITIL practices that matter most to your organization. Then answer the operating questions behind them. How should incidents be classified and prioritized? What qualifies as a service request? When does an incident become a problem? Who owns each stage? Which changes need review or approval? 

These decisions form the foundation for effective service management. They apply whether you are implementing JSM for the first time or improving an existing environment. 

2. Standardize before you customize 

It’s tempting to recreate every workflow and workaround from a legacy service desk. Resist that instinct. 

Use proven ITSM and ITIL best practices as your starting point. Then decide where your organization has legitimate reasons to vary. The goal is to build a better service desk, rather than reproduce the old one on a new platform. 

3. Build capability alongside the technology 

Process maturity doesn’t come from configuration alone. Teams need to understand the practices they’re adopting and why those practices matter. 

That means giving service teams the knowledge to manage incidents, problems, changes, and requests consistently. It also means building workflows that make those practices easier to follow. For teams that need stronger internal ITIL knowledge, Cprime offers ITIL 5 Foundation training alongside transformation work. 

Questions to answer before you configure JSM 

Before you build workflows, automation, or custom fields, make sure your team can answer these questions: 

• What does good incident management look like for our organization? 

• How do we distinguish an incident, a problem, and a service request? 

• Who owns each stage of the service management process? 

• What is our change enablement process? 

• Which processes should be standardized across teams? 

• Where do we genuinely need to customize? 

• What should we automate, and what still requires human judgment? 

• How will we measure whether service delivery is improving? 

If these questions lack clear answers, configuration shouldn’t be the first step. 

What an ITIL-aligned service management environment looks like 

An effective service management transformation brings process and technology together in three layers. 

Establish the foundation: Define and align core ITSM practices using ITIL as the framework. Account for your organization’s specific services, teams, and operating model. 

Configure for the way work should happen: Translate those practices into JSM workflows, request types, queues, SLAs, automation, and reporting. Avoid over-customizing the platform. 

Enable the people who run it: Give teams the training to use new processes well and keep improving them after go-live. 

For organizations with more complex needs, this can extend into broader service management transformation. JSM then connects development, operations, and business teams into a more consistent service experience across the enterprise. 

How Cprime brings ITIL expertise to service management 

As an Atlassian Platinum Solution Partner with an ITSM specialization, Cprime applies deep ITIL expertise across its service management work. Whether you’re implementing JSM, migrating from a legacy desk, or fixing an underperforming operation, we start with the process. 

Our teams bring practical ITIL knowledge to assessments, process design, workflow development, JSM implementations, migrations, and ongoing optimization. We help you find where service management is breaking down and establish more effective practices. Then we use technology to make those practices scalable and measurable. 

As a result, you don’t have to choose between process expertise and technology expertise. Cprime brings both together, so service management works for the people who run it and the business they support. 

To go deeper, read Cprime and ITIL: The Key to Unlock Optimized ITSM. 

Key takeaways 

• Atlassian Rovo improves delivery only when it is designed into sprint, release, and dependency workflows. 

• DORA’s 2025 research finds that AI amplifies an organization’s existing strengths and weaknesses. 

• Four gaps stall delivery impact: design, integration, validation, and adoption. 

• Closing those gaps turns Rovo from AI search into delivery intelligence that leaders can measure. 

For delivery leaders, the next question is how that capability translates into measurable improvements in delivery. 

Are teams identifying risks earlier? Are sprints becoming more predictable? Is less time spent assembling status updates? 

Access to AI tools alone doesn’t guarantee these outcomes. Realizing value depends on how teams use Rovo within their existing delivery processes and workflows. 

This article explores the gap between AI access and delivery impact, what recent research suggests, and how organizations can better connect Rovo to the work of planning, coordinating, and delivering.  

Why doesn’t Atlassian Rovo improve delivery on its own? 

Atlassian Rovo improves delivery only when it is built into the workflows where delivery decisions happen. Out of the box, Rovo offers AI search, chat, and general-purpose agents. Delivery gains need more: custom agents, connected DevOps signals, validated outputs, and routines that use them. 

Most rollouts stop at activation. Teams use Rovo Search to find a Confluence page or ask Rovo Chat to summarize a Jira epic. That saves time for individuals. Yet it does not tell a release manager whether Friday’s release is at risk. 

In other words, Rovo AI becomes a faster way to find information. Delivery intelligence still has to be designed, integrated, and adopted. Our guide to getting started with Atlassian Rovo agents covers the activation stage in more detail. 

What the research says about AI and software delivery performance 

This pattern extends well beyond Rovo. Google Cloud’s DORA program surveyed nearly 5,000 technology professionals for its 2025 State of AI-assisted Software Development report. Its central finding is that AI acts as an amplifier. It magnifies the strengths and weaknesses an organization already has. 

DORA also found that AI is now linked to higher delivery throughput. At the same time, AI continues to increase delivery instability. Teams have sped up, while the systems around them have not caught up. 

Atlassian’s 2025 State of Developer Experience research shows the same tension. Sixty-eight percent of developers save more than 10 hours a week with AI. Meanwhile, half of developers lose more than 10 hours a week to organizational inefficiencies. 

Developers also spend only about 16% of their time coding. Their biggest time drains are finding information, adapting to new technology, and switching between tools. 

This is the AI productivity paradox in delivery terms. Time saved in one step gets absorbed by friction in the next. Without deliberate design, extra AI output can bury the signal leaders need under more noise. 

The four gaps between Rovo activation and delivery impact 

Across Atlassian environments, the same four gaps keep Rovo from changing delivery outcomes. 

1. The design gap 

Agile coaches and delivery leads usually know which insights teams need. Examples include sprint health, carryover risk, blocked dependencies, and release readiness. However, that knowledge rarely becomes working Rovo agents. 

Rovo Studio makes agent creation more accessible. Still, production-grade Rovo Actions often require Forge development skills that many delivery teams lack. 

2. The integration gap 

Delivery signals live in many places, including Jira, Bitbucket, CI/CD pipelines, and testing platforms. When those signals stay disconnected, Rovo agents cannot reason about readiness or risk. 

Atlassian’s Teamwork Graph links work, people, and knowledge into shared context, as our Atlassian Team ’26 recap explains. Even so, delivery data from outside tools has to be connected and structured deliberately. 

3. The validation gap 

Teams act on AI insights only when they trust them. Trust is already a hurdle for AI in software teams. In DORA’s 2025 research, 30% of respondents reported little or no trust in AI-generated code. 

Delivery insights face the same test. If a sprint summary misstates carryover once, teams stop reading it. Validating agent outputs against real delivery data keeps that trust intact. 

4. The adoption gap 

Insights change outcomes only when they appear where decisions happen. Those moments include stand-ups, sprint reviews, release go/no-go calls, and portfolio check-ins. 

If agent outputs live in a separate chat window, usage fades within weeks. Adoption has to be designed into team routines and reinforced over time. Structured AI adoption and change coaching provides that continuity, so the gains last. 

What closes the gap: from AI search to delivery intelligence 

Delivery intelligence turns live work and DevOps data into trusted insights inside delivery workflows. On Atlassian, it combines Rovo agents, connected signals, validation, and adoption design. Organizations that close the gap tend to follow four moves. 

Start with delivery decisions 

Identify the decisions that slow delivery today. Common examples are sprint commitments, release readiness, and cross-team dependency calls. Then define the insight each decision needs and who acts on it. 

Connect the signals 

Integrate Jira, Bitbucket, CI/CD, and test data into the context Rovo agents use. As a result, agents gain the evidence they need to flag risk early in the sprint. 

Build and validate focused agents 

Build agents for high-value workflows first, such as automated sprint summaries or release notes drafted from merged pull requests. Test each output against real delivery data before teams rely on it. 

Embed, measure, and expand 

Place agent outputs inside existing ceremonies and dashboards. Baseline delivery KPIs first, then track change over several sprints. Once the first agents prove their value, expand to new workflows. 

The result is faster decision flow. Teams spend fewer hours assembling status and see earlier signal on the risks that matter. 

How do you measure whether Atlassian Rovo is improving delivery? 

Measure delivery outcomes, and treat usage data as a leading indicator only. Useful metrics include reporting effort per sprint, cycle time, sprint carryover, delay frequency, and time to answer status requests. 

DORA metrics add a stability lens. Change failure rate and deployment rework rate show whether faster delivery is also safe delivery. 

Set a baseline before the first pilot. Then compare results sprint by sprint, so leaders gain value visibility in delivery terms they already use. 

How Cprime helps 

Cprime helps engineering and delivery leaders make Atlassian Rovo operational through Rovo-Augmented Product Development. As Atlassian’s largest solution partner for more than 12 years, Cprime brings Atlassian, Agile delivery, and AI implementation expertise together. 

Every engagement starts with a Rovo delivery workflow assessment. It identifies where agents can generate actionable insights, where DevOps signals are disconnected, and which validation risks affect accuracy. 

From there, Cprime designs, builds, and validates Rovo agents inside sprint, release, and dependency workflows. The work runs as an ongoing program, so adoption holds as teams and priorities change. 

Clients typically see 70% less sprint reporting effort and 40 to 60% fewer delivery delays. Teams also recover more than five hours per sprint for delivery work. Explore our broader Atlassian Rovo services to see how this connects to enterprise-wide AI adoption. 

Most software teams already use AI. Developers accept code suggestions, and product managers draft user stories from a prompt. Yet delivery rarely gets faster in a way leadership can measure. 

The reason is simple. Coding was never the only bottleneck. Instead, delays pile up in the handoffs: refining requirements, chasing dependencies, reviewing code, writing release notes, and assembling status updates. 

Agentic SDLC targets exactly those gaps. Rather than helping one person type faster, it puts AI agents to work across the whole software delivery lifecycle. In the Atlassian ecosystem, those agents are Rovo agents. 

Below, we explain what agentic SDLC means, how Rovo agents reshape each stage of delivery, and what it takes to make them produce real results. 

What is agentic SDLC? 

Agentic SDLC is a software delivery model where AI agents plan, execute, and coordinate work across the lifecycle. Humans set direction, review outputs, and own the decisions. 

Traditional SDLC relies on people to move work between phases. AI-assisted development speeds up individual tasks inside those phases. Agentic SDLC goes further, because agents also carry work across the boundaries between phases. 

Why the software delivery lifecycle needs agents now 

Generative AI software development tools made coding faster. However, a faster coder doesn’t guarantee a faster release. Work still waits on refinement, reviews, approvals, and reporting. 

Meanwhile, the context teams need is scattered. Requirements sit in Confluence, work sits in Jira, and code sits in Bitbucket or GitHub. Pipeline signals live somewhere else again. As a result, people spend hours stitching that context together by hand. 

Agents change the economics of that stitching. Because they can read across systems and act on what they find, they absorb the coordination work that slows delivery. Engineers and product leaders then get time back for design, judgment, and problem-solving. 

Where Rovo agents fit in an agentic SDLC 

Atlassian Rovo is the AI layer built into the Atlassian Cloud platform. It powers search, chat, and agents across Jira, Confluence, and connected tools. Four parts matter most for agentic SDLC. 

• Teamwork Graph. Rovo draws on Atlassian’s Teamwork Graph to connect context across Jira, Confluence, Bitbucket, Compass, and more. Agents therefore understand how work connects, not just what a single ticket says. 

• Rovo agents. These task-specific assistants handle workflows like issue triage, backlog grooming, and documentation edits, and teams can trigger them via chat, automation rules, or the /ai command. 

• Rovo Studio. Studio is a no-code/low-code workspace for defining agent behavior, knowledge sources, and actions, while developers can go deeper with Forge. 

• Rovo Dev. Atlassian positions Rovo Dev as a context-aware agent that handles planning, coding, and reviews, available in the terminal, the IDE, and beyond. 

Together, these pieces give teams the raw material for an agentic SDLC. Still, raw material isn’t a working system, as the later sections explain. 

How Rovo agents reshape each stage of delivery 

1. Planning and requirements 

Backlog refinement is often where velocity stalls first. Rovo agents can turn a rough idea or Confluence brief into structured Jira work items. They can also suggest acceptance criteria, subtasks, and likely dependencies. 

Product managers still decide what matters. However, they start from a solid draft instead of a blank ticket. Refinement sessions then focus on trade-offs rather than typing. 

2. Design and code planning 

Before writing code, developers often ramp up across five or six tools. Rovo Dev shortens that ramp with code plans that pull knowledge from across the Atlassian platform. 

Consequently, new team members onboard faster. Experienced engineers, meanwhile, spend less time hunting for context. 

3. Build 

During implementation, Rovo Dev can generate code aligned to the approved plan, including refactoring, new tests, and docs. It works in the terminal and IDE, so developers stay in their normal flow. 

The real difference from generic coding assistants is context. Because Rovo Dev draws on the Teamwork Graph, it knows why a change exists, not just what the file contains. 

4. Code review and quality 

Review queues are a classic bottleneck. Rovo Dev can analyze code, suggest improvements, and validate changes against acceptance criteria in Jira. 

This shifts quality left. Reviewers focus on architecture and risk, while the agent handles consistency checks and obvious gaps. 

5. Release and documentation 

Release notes rarely get anyone’s full attention, yet they can hold up deployments. Custom Rovo agents can draft notes from merged pull requests and completed work items. Similarly, they can assemble readiness summaries for change approvals. 

Teams still sign off on every release. Nevertheless, the documentation is ready when the code is. 

6. Delivery intelligence and status reporting 

The final stage closes the loop. Custom agents can generate sprint summaries from live delivery data and flag cross-team dependencies. They can also surface risk before a commitment slips. 

Leaders get current answers without calling another status meeting. Over time, each sprint becomes a feedback loop that improves predictability. This is where agentic SDLC meets value stream management and software delivery management. 

What an agentic SDLC requires to work 

Organizations are rapidly enabling Rovo after moving to Atlassian Cloud, and leadership expects it to improve delivery visibility. Yet most teams use it mainly for search. On its own, that won’t change delivery performance. Four gaps usually stand in the way. 

Insight design: Agents need a clearly defined job. Someone has to decide which sprint, release, and dependency insights actually help teams decide faster. 

Integration: Delivery signals live in Jira, Bitbucket, CI/CD pipelines, and testing tools. If those signals never reach Rovo, agents can’t produce meaningful insights. 

Engineering capability: Rovo Actions require Forge development expertise many teams do not have. Without it, agents stay limited to reading rather than acting. 

Validation and trust: AI outputs must be checked against real delivery data. Otherwise, teams quietly stop trusting the agent, and then stop using it. 

Governance runs across all four. Clear approval points, audit trails, and access policies keep agents accountable. Helpfully, Rovo only accesses data a user is already permitted to see. 

Humans stay in charge 

Agentic doesn’t mean unsupervised. The strongest implementations keep people accountable for priorities, architecture, and release decisions. Agents take on repetitive, context-heavy work, so humans can apply judgment where it counts. 

That balance also drives adoption. When engineers see agents removing toil rather than second-guessing them, usage grows on its own. 

From Rovo features to delivery outcomes 

None of these capabilities are out of reach. Rovo agents, Rovo Studio, and Rovo Dev are available on the Atlassian platform today. Still, they only reshape delivery when someone designs, connects, and validates them inside real workflows. 

That’s why so many teams stall after the pilot. Production-grade agents require Atlassian, Agile, DevOps, and AI skills that rarely sit in one team. 

Introducing Rovo-Augmented Product Development 

Rovo-Augmented Product Development is Cprime’s approach to turning Rovo into a working delivery intelligence layer. We design, build, and validate Rovo agents inside sprint, release, and DevOps workflows. 

Every engagement begins with a Rovo delivery workflow assessment. It identifies workflows where agents can generate actionable insights, gaps in DevOps signal integration, data and validation risks, and initial agents that can show measurable impact. 

The outcomes are concrete: 

• Sprint intelligence: 70% less reporting effort, with more than five hours freed per team per sprint. 

• Release documentation: Release notes produced from merged pull requests and completed work, accelerating documentation cycles by 90%. 

• Dependency detection: Clients typically see 40 to 60 percent fewer delivery delays. 

• Real-time status: Status response accelerates by up to 95 percent, and meetings shorten. 

As an Atlassian Platinum Solution Partner, Cprime brings the Forge, DevOps, and adoption expertise that turns Rovo from a search tool into a delivery advantage.