Scaling agentic AI isn’t nearly having the most recent instruments — it requires clear steerage, the suitable context, and a tradition that champions experimentation to unlock actual worth. At VentureBeat’s Remodel 2025, Anu Bharadwaj, president of Atlassian, shared actionable insights into how the corporate has empowered its workers to construct hundreds of customized brokers that resolve 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 mentioned. “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 each day work, however folks have to make use of it of their context and iterate over time to understand 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 circumstances.
Making a protected 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 essential for us to create a psychologically protected surroundings,” Bharadwaj mentioned. “At Atlassian, we’ve at all times been very open. Open firm, no bullshit is one in all our values. So we concentrate on creating that openness, and creating an surroundings the place workers can check out various things, and if it fails, it’s okay. It’s superb since you discovered one thing about the best way to use AI in your context. It’s useful to be very express and open about it.”
Past that, you must create a steadiness between experimentation with guardrails of security and auditability. This consists of security measures like ensuring workers are logged in once they’re making an attempt instruments, to creating positive brokers respect permissions, perceive role-based entry, and supply solutions and actions based mostly on what a specific 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 mentioned. “What does teamwork seem like throughout a workforce 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? Because of this, all of our groups use Rovo brokers and construct their very own Rovo brokers. Our concept is that when that form of teamwork turns into extra commonplace, the complete working system of the corporate modifications.”
The magic actually occurs when a number of folks work along with a number of brokers, she added. At present plenty of brokers are single-player, however interplay patterns are evolving. Chat won’t be the default interplay sample, Bharadwaj says. As an alternative, there will likely be a number of interplay patterns that drive multiplayer collaboration.
“Essentially, 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 obtainable and accessible to folks 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 big HarperCollins constructed brokers that lowered handbook 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 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 speak about agentic transformation, it can’t be restricted to the code gen eventualities we see as we speak. It has to permeate the complete workforce,” Bharadwaj mentioned. “Builders spend 10% of their time coding. The remaining 90% is working with the remainder of the workforce, determining buyer points and fixing points in manufacturing. We’re making a platform via which you’ll be able to construct brokers for each single a kind of capabilities, so the complete loop will get sooner.”
Making a bridge from right here to the long run
In contrast to the earlier shifts to cellular or cloud, the place a set of technological or go-to-market modifications occurred, AI transformation is basically a change in the best way we work. Bharadwaj believes an important factor to do is to be open and to share how you might be utilizing AI to vary your each day work. “For example, 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 believed, oh, this may very well be helpful if solely it had the suitable context,” she added. “That fixed psychological iteration, for workers to see and take a look at each single day, is very essential as we shift the best way we work.”
