Scaling agentic AI isn’t nearly having the most recent instruments — it requires clear steerage, 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 workers to construct 1000’s 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 quite a bit about AI top-down mandates,” Bharadwaj stated. “Prime-down mandates are nice for making an enormous splash, however actually, what occurs subsequent, and to who? Brokers require fixed iteration and adaptation. Prime-down mandates can encourage folks to start out utilizing it of their day by day work, however folks have to make use of it of their context and iterate over time to comprehend 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 development and high-impact use instances.
Making a secure surroundings
Atlassian’s agent-building platform, Rovo Studio, serves as a playground surroundings for groups throughout the enterprise to construct brokers.
“As leaders, it’s necessary for us to create a psychologically secure surroundings,” Bharadwaj stated. “At Atlassian, we’ve at all times been very open. Open firm, no bullshit is one in all our values. So we deal with creating that openness, and creating an surroundings the place workers can check out various things, and if it fails, it’s okay. It’s fantastic since you realized one thing about use AI in your context. It’s useful to be very specific and open about it.”
Past that, you must create a stability between experimentation with guardrails of security and auditability. This consists of security measures like ensuring workers are logged in after they’re making an attempt instruments, to creating certain brokers respect permissions, perceive role-based entry, and supply solutions and actions based mostly on what a selected consumer has entry to.
Supporting team-agent collaboration
“After we take into consideration brokers, we take into consideration how people and brokers work collectively,” Bharadwaj stated. “What does teamwork seem like throughout a group composed of a bunch of individuals and a bunch of brokers — and the way does that evolve over time? What can we do to help that? Consequently, all of our groups use Rovo brokers and construct their very own Rovo brokers. Our concept is that after that sort of teamwork turns into extra commonplace, your entire working system of the corporate modifications.”
The magic actually occurs when a number of folks work along with a number of brokers, she added. As we speak plenty of brokers are single-player, however interplay patterns are evolving. Chat won’t be the default interplay sample, Bharadwaj says. As a substitute, there will likely 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 folks of all ability units, together with no-code choices. One development trade buyer constructed a set of brokers to cut back their roadmap creation time by 75%, whereas publishing big HarperCollins constructed brokers that decreased 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 data sources, shaping interplay patterns and extra — and create extremely specialised brokers. On the identical time, non-technical groups additionally must 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 essentially, once we speak about agentic transformation, it can’t be restricted to the code gen situations we see at this time. It has to permeate your entire group,” Bharadwaj stated. “Builders spend 10% of their time coding. The remaining 90% is working with the remainder of the group, determining buyer points and fixing points in manufacturing. We’re making a platform by which you’ll be able to construct brokers for each single a kind of features, so your entire loop will get quicker.”
Making a bridge from right here to the long run
Not like the earlier shifts to cellular or cloud, the place a set of technological or go-to-market modifications occurred, AI transformation is essentially a change in the way in which we work. Bharadwaj believes a very powerful factor to do is to be open and to share how you might be utilizing AI to vary your day by 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 could possibly 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.”
