Scaling agentic AI isn’t nearly having the newest instruments — it requires clear steerage, the fitting 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 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 quite a bit about AI top-down mandates,” Bharadwaj mentioned. “High-down mandates are nice for making a giant splash, however actually, what occurs subsequent, and to who? Brokers require fixed iteration and adaptation. High-down mandates can encourage individuals to start out utilizing it of their day by day work, however individuals 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 essential for us to create a psychologically protected surroundings,” Bharadwaj mentioned. “At Atlassian, we’ve all the time been very open. Open firm, no bullshit is certainly one of 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 tremendous since you discovered one thing about find out how 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 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 primarily based 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 mentioned. “What does teamwork appear to be 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 assist that? In consequence, all of our groups use Rovo brokers and construct their very own Rovo brokers. Our concept is that after that type of teamwork turns into extra commonplace, your complete working system of the corporate modifications.”
The magic actually occurs when a number of individuals work along with a number of brokers, she added. At the moment lots of brokers are single-player, however interplay patterns are evolving. Chat won’t be the default interplay sample, Bharadwaj says. As a substitute, there can 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 individuals of all talent units, together with no-code choices. One building business buyer constructed a set of brokers to scale back their roadmap creation time by 75%, whereas publishing big HarperCollins constructed brokers that decreased 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 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 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 as we speak. It has to permeate your complete group,” Bharadwaj mentioned. “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 way of which you’ll be able to construct brokers for each single a kind of features, so your complete loop will get sooner.”
Making a bridge from right here to the long run
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 essentially a change in the best way we work. Bharadwaj believes crucial factor to do is to be open and to share how you’re utilizing AI to alter your day by 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 assumed, oh, this could possibly be helpful if solely it had the fitting 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.”
