Scaling agentic AI isn’t nearly having the most recent instruments — it requires clear steering, the proper 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 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. “Prime-down mandates are nice for making a giant 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 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 progress and high-impact use instances.
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 necessary for us to create a psychologically protected surroundings,” Bharadwaj stated. “At Atlassian, we’ve all the time been very open. Open firm, no bullshit is certainly one of our values. So we give attention to creating that openness, and creating an surroundings the place staff can check out various things, and if it fails, it’s okay. It’s high-quality since you realized one thing about the right way to use AI in your context. It’s useful to be very express and open about it.”
Past that, you need to create a steadiness between experimentation with guardrails of security and auditability. This contains security measures like ensuring staff are logged in after they’re making an attempt instruments, to creating positive brokers respect permissions, perceive role-based entry, and supply solutions and actions primarily based on what a selected consumer 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 appear 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 principle is that after that sort of teamwork turns into extra commonplace, your complete working system of the corporate adjustments.”
The magic actually occurs when a number of folks work along with a number of brokers, she added. At this time a variety of brokers are single-player, however interplay patterns are evolving. Chat is not going to be the default interplay sample, Bharadwaj says. As a substitute, 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 obtainable and accessible to folks of all ability units, together with no-code choices. One building trade buyer constructed a set of brokers to cut back their roadmap creation time by 75%, whereas publishing large HarperCollins constructed brokers that diminished guide work by 4X throughout their departments.
By combining Rovo Studio with their developer platform, Forge, technical groups achieve 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 similar 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 massive unlock, as a result of basically, after we speak about agentic transformation, it can’t be restricted to the code gen situations we see right now. It has to permeate your complete 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 means of which you’ll be able to construct brokers for each single a type of capabilities, so your complete 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 adjustments occurred, AI transformation is basically a change in the best way we work. Bharadwaj believes crucial factor to do is to be open and to share how you might be utilizing AI to alter 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 could possibly be helpful if solely it had the proper context,” she added. “That fixed psychological iteration, for workers to see and check out each single day, is very necessary as we shift the best way we work.”
