Scaling agentic AI isn’t nearly having the newest instruments — it requires clear steering, the precise context, and a tradition that champions experimentation to unlock actual worth. At VentureBeat’s Rework 2025, Anu Bharadwaj, president of Atlassian, shared actionable insights into how the corporate has empowered its staff to construct hundreds of customized brokers that clear up actual, on a regular basis challenges. To construct these brokers, Atlassian has fostered a tradition rooted in curiosity, enthusiasm and steady experimentation.
“You hear loads about AI top-down mandates,” Bharadwaj stated. “High-down mandates are nice for making an enormous splash, however actually, what occurs subsequent, and to who? Brokers require fixed iteration and adaptation. High-down mandates can encourage individuals to begin utilizing it of their every day work, however individuals have to make use of it of their context and iterate over time to appreciate most worth.”
That requires a tradition of experimentation — one the place short- to medium-term setbacks aren’t penalized however embraced as stepping stones to future progress and high-impact use instances.
Making a secure setting
Atlassian’s agent-building platform, Rovo Studio, serves as a playground setting for groups throughout the enterprise to construct brokers.
“As leaders, it’s necessary for us to create a psychologically secure setting,” Bharadwaj stated. “At Atlassian, we’ve at all times been very open. Open firm, no bullshit is considered one of our values. So we deal with creating that openness, and creating an setting the place staff can check out various things, and if it fails, it’s okay. It’s superb since you discovered one thing about use AI in your context. It’s useful to be very express and open about it.”
Past that, it’s important to create a stability between experimentation with guardrails of security and auditability. This contains security measures like ensuring staff are logged in after they’re attempting instruments, to creating positive brokers respect permissions, perceive role-based entry, and supply solutions and actions based mostly on what a selected person has entry to.
Supporting team-agent collaboration
“Once we take into consideration brokers, we take into consideration how people and brokers work collectively,” Bharadwaj stated. “What does teamwork seem like throughout a crew composed of a bunch of individuals and a bunch of brokers — and the way does that evolve over time? What can we do to assist that? Because of this, all of our groups use Rovo brokers and construct their very own Rovo brokers. Our concept is that when that sort of teamwork turns into extra commonplace, all the working system of the corporate modifications.”
The magic actually occurs when a number of individuals work along with a number of brokers, she added. As we speak loads of brokers are single-player, however interplay patterns are evolving. Chat won’t be the default interplay sample, Bharadwaj says. As an alternative, there might be a number of interplay patterns that drive multiplayer collaboration.
“Basically, what’s teamwork all about?” she posed to the viewers. “It’s multiplayer collaboration — a number of brokers and a number of people working collectively.”
Making agent experimentation accessible
Atlassian’s Rovo Studio makes agent constructing out there and accessible to individuals of all ability units, together with no-code choices. One development business buyer constructed a set of brokers to scale back their roadmap creation time by 75%, whereas publishing large HarperCollins constructed brokers that lowered guide work by 4X throughout their departments.
By combining Rovo Studio with their developer platform, Forge, technical groups acquire highly effective management to deeply customise their AI workflows — defining context, specifying accessible information sources, shaping interplay patterns and extra — and create extremely specialised brokers. On the identical time, non-technical groups additionally have to customise and iterate, so that they’ve constructed experiences in Rovo Studio to permit customers to leverage pure language to make their customizations.
“That’s going to be the large unlock, as a result of basically, after we discuss agentic transformation, it can’t be restricted to the code gen eventualities we see immediately. It has to permeate all the crew,” Bharadwaj stated. “Builders spend 10% of their time coding. The remaining 90% is working with the remainder of the crew, determining buyer points and fixing points in manufacturing. We’re making a platform by way of which you’ll be able to construct brokers for each single a type of features, so all the loop will get sooner.”
Making a bridge from right here to the longer term
In contrast to the earlier shifts to cell or cloud, the place a set of technological or go-to-market modifications occurred, AI transformation is basically a change in the way in which we work. Bharadwaj believes crucial factor to do is to be open and to share how you might be utilizing AI to alter your every day work. “For instance, I share Loom movies of latest instruments that I’ve tried out, issues that I like, issues that I didn’t like, issues the place I assumed, oh, this might be helpful if solely it had the precise context,” she added. “That fixed psychological iteration, for workers to see and check out each single day, is extremely necessary as we shift the way in which we work.”