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 Remodel 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 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 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 every day work, however folks 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 protected atmosphere
Atlassian’s agent-building platform, Rovo Studio, serves as a playground atmosphere for groups throughout the enterprise to construct brokers.
“As leaders, it’s vital for us to create a psychologically protected atmosphere,” Bharadwaj stated. “At Atlassian, we’ve at all times been very open. Open firm, no bullshit is one in every of our values. So we concentrate on creating that openness, and creating an atmosphere the place workers can check out various things, and if it fails, it’s okay. It’s high quality since you discovered one thing about how you can use AI in your context. It’s useful to be very express and open about it.”
Past that, it’s important to create a steadiness between experimentation with guardrails of security and auditability. This contains 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 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 idea is that when that sort of teamwork turns into extra commonplace, your entire working system of the corporate adjustments.”
The magic actually occurs when a number of folks work along with a number of brokers, she added. As we speak numerous brokers are single-player, however interplay patterns are evolving. Chat is not going to be the default interplay sample, Bharadwaj says. As an alternative, 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 obtainable and accessible to folks of all ability units, together with no-code choices. One development business buyer constructed a set of brokers to cut 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 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 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 massive unlock, as a result of essentially, once we discuss agentic transformation, it can’t be restricted to the code gen situations we see right this moment. It has to permeate your entire 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 via which you’ll be able to construct brokers for each single a type of features, so your entire loop will get quicker.”
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 adjustments occurred, AI transformation is essentially a change in the way in which 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 every day work. “For example, I share Loom movies of recent instruments that I’ve tried out, issues that I like, issues that I didn’t like, issues the place I believed, oh, this might be helpful if solely it had the precise context,” she added. “That fixed psychological iteration, for workers to see and take a look at each single day, is extremely vital as we shift the way in which we work.”
