Scaling agentic AI: Inside Atlassian’s culture of experimentation

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Scaling agentic AI: Inside Atlassian’s culture of experimentation

Scaling agentic AI isn’t just about having the latest tools — it requires clear guidance, the right context, and a culture that champions experimentation to unlock real value. At VentureBeat’s Transform 2025, Anu Bharadwaj, president of Atlassian, shared actionable insights into how the company has empowered its employees to build thousands of custom agents that solve real, everyday challenges. To build these agents, Atlassian has fostered a culture rooted in curiosity, enthusiasm and continuous experimentation. [embedded content] “You hear a lot about AI top-down mandates,” Bharadwaj said. “Top-down mandates are great for making a big splash, but really, what happens next, and to who? Agents require constant iteration and adaptation. Top-down mandates can encourage people to start using it in their daily work, but people have to use it in their context and iterate over time to realize maximum value.” That requires a culture of experimentation — one where short- to medium-term setbacks aren’t penalized but embraced as stepping stones to future growth and high-impact use cases. Creating a safe environment Atlassian’s agent-building platform, Rovo Studio, serves as a playground environment for teams across the enterprise to build agents. “As leaders, it’s important for us to create a psychologically safe environment,” Bharadwaj said. “At Atlassian, we’ve always been very open. Open company, no bullshit is one of our values. So we focus on creating that openness, and creating an environment where employees can try out different things, and if it fails, it’s okay. It’s fine because you learned something about how to use AI in your context. It’s helpful to be very explicit and open about it.” Beyond that, you have to create a balance between experimentation with guardrails of safety and auditability. This includes safety measures like making sure employees are logged in when they’re trying tools, to making sure agents respect permissions, understand role-based access, and provide answers and actions based on what a particular user has access to. Supporting team-agent collaboration “When we think about agents, we think about how humans and agents work together,” Bharadwaj said. “What does teamwork look like across a team composed of a bunch of people and a bunch of agents — and how does that evolve over time? What can we do to support that? As a result, all of our teams use Rovo agents and build their own Rovo agents. Our theory is that once that kind of teamwork becomes more commonplace, the entire operating system of the company changes.” The magic really happens when multiple people work together with multiple agents, she added. Today a lot of agents are single-player, but interaction patterns are evolving. Chat will not be the default interaction pattern, Bharadwaj says. Instead, there will be multiple interaction patterns that drive multiplayer collaboration. “Fundamentally, what is teamwork all about?” she posed to the audience. “It’s multiplayer collaboration — multiple agents and multiple humans working together.” Making agent experimentation accessible Atlassian’s Rovo Studio makes agent building available and accessible to people of all skill sets, including no-code options. One construction industry customer built a set of agents to reduce their roadmap creation time by 75%, while publishing giant HarperCollins built agents that reduced manual work by 4X across their departments. By combining Rovo Studio with their developer platform, Forge, technical teams gain powerful control to deeply customize their AI workflows — defining context, specifying accessible knowledge sources, shaping interaction patterns and more — and create highly specialized agents. At the same time, non-technical teams also need to customize and iterate, so they’ve built experiences in Rovo Studio to allow users to leverage natural language to make their customizations. “That’s going to be the big unlock, because fundamentally, when we talk about agentic transformation, it cannot be restricted to the code gen scenarios we see today. It has to permeate the entire team,” Bharadwaj said. “Developers spend 10% of their time coding. The remaining 90% is working with the rest of the team, figuring out customer issues and fixing issues in production. We’re creating a platform through which you can build agents for every single one of those functions, so the entire loop gets faster.” Creating a bridge from here to the future Unlike the previous shifts to mobile or cloud, where a set of technological or go-to-market changes occurred, AI transformation is fundamentally a change in the way we work. Bharadwaj believes the most important thing to do is to be open and to share how you are using AI to change your daily work. “As an example, I share Loom videos of new tools that I’ve tried out, things that I like, things that I didn’t like, things where I thought, oh, this could be useful if only it had the right context,” she added. “That constant mental iteration, for employees to see and try every single day, is highly important as we shift the way we work.”

“You hear a lot about AI top-down mandates,” Bharadwaj said. “Top-down mandates are great for making a big splash, but really, what happens next, and to who? Agents require constant iteration and adaptation. Top-down mandates can encourage people to start using it in their daily work, but people have to use it in their context and iterate over time to realize maximum value.”

That requires a culture of experimentation — one where short- to medium-term setbacks aren’t penalized but embraced as stepping stones to future growth and high-impact use cases.

Creating a safe environment

Atlassian’s agent-building platform, Rovo Studio, serves as a playground environment for teams across the enterprise to build agents.

“As leaders, it’s important for us to create a psychologically safe environment,” Bharadwaj said. “At Atlassian, we’ve always been very open. Open company, no bullshit is one of our values. So we focus on creating that openness, and creating an environment where employees can try out different things, and if it fails, it’s okay. It’s fine because you learned something about how to use AI in your context. It’s helpful to be very explicit and open about it.”

Beyond that, you have to create a balance between experimentation with guardrails of safety and auditability. This includes safety measures like making sure employees are logged in when they’re trying tools, to making sure agents respect permissions, understand role-based access, and provide answers and actions based on what a particular user has access to.

Supporting team-agent collaboration

“When we think about agents, we think about how humans and agents work together,” Bharadwaj said. “What does teamwork look like across a team composed of a bunch of people and a bunch of agents — and how does that evolve over time? What can we do to support that? As a result, all of our teams use Rovo agents and build their own Rovo agents. Our theory is that once that kind of teamwork becomes more commonplace, the entire operating system of the company changes.”

The magic really happens when multiple people work together with multiple agents, she added. Today a lot of agents are single-player, but interaction patterns are evolving. Chat will not be the default interaction pattern, Bharadwaj says. Instead, there will be multiple interaction patterns that drive multiplayer collaboration.

“Fundamentally, what is teamwork all about?” she posed to the audience. “It’s multiplayer collaboration — multiple agents and multiple humans working together.”

Making agent experimentation accessible

Atlassian’s Rovo Studio makes agent building available and accessible to people of all skill sets, including no-code options. One construction industry customer built a set of agents to reduce their roadmap creation time by 75%, while publishing giant HarperCollins built agents that reduced manual work by 4X across their departments.

By combining Rovo Studio with their developer platform, Forge, technical teams gain powerful control to deeply customize their AI workflows — defining context, specifying accessible knowledge sources, shaping interaction patterns and more — and create highly specialized agents. At the same time, non-technical teams also need to customize and iterate, so they’ve built experiences in Rovo Studio to allow users to leverage natural language to make their customizations.

“That’s going to be the big unlock, because fundamentally, when we talk about agentic transformation, it cannot be restricted to the code gen scenarios we see today. It has to permeate the entire team,” Bharadwaj said. “Developers spend 10% of their time coding. The remaining 90% is working with the rest of the team, figuring out customer issues and fixing issues in production. We’re creating a platform through which you can build agents for every single one of those functions, so the entire loop gets faster.”

Creating a bridge from here to the future

Unlike the previous shifts to mobile or cloud, where a set of technological or go-to-market changes occurred, AI transformation is fundamentally a change in the way we work. Bharadwaj believes the most important thing to do is to be open and to share how you are using AI to change your daily work. “As an example, I share Loom videos of new tools that I’ve tried out, things that I like, things that I didn’t like, things where I thought, oh, this could be useful if only it had the right context,” she added. “That constant mental iteration, for employees to see and try every single day, is highly important as we shift the way we work.”

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What this doesn’t answer is how Arista Networks plans to add newer, security-oriented Secure Access Service Edge (SASE) capabilities to VeloCloud’s older SD-WAN technology. Post-acquisition, it still has only some of the building blocks necessary to achieve this. Mapping AI However, in 2025 there is always more going on with networking acquisitions than simply adding another brick to the wall, and in this case it’s the way AI is changing data flows across networks. “In the new AI era, the concepts of what comprises a user and a site in a WAN have changed fundamentally. The introduction of agentic AI even changes what might be considered a user,” wrote Arista Networks CEO, Jayshree Ullal, in a blog highlighting AI’s effect on WAN architectures. “In addition to people accessing data on demand, new AI agents will be deployed to access data independently, adapting over time to solve problems and enhance user productivity,” she said. Specifically, WANs needed modernization to cope with the effect AI traffic flows are having on data center traffic. Sanjay Uppal, now VP and general manager of the new VeloCloud Division at Arista Networks, elaborated. “The next step in SD-WAN is to identify, secure and optimize agentic AI traffic across that distributed enterprise, this time from all end points across to branches, campus sites, and the different data center locations, both public and private,” he wrote. “The best way to grab this opportunity was in partnership with a networking systems leader, as customers were increasingly looking for a comprehensive solution from LAN/Campus across the WAN to the data center.”

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However, even though colocation and on-premises data centers will continue to lose share, they will still continue to grow. They just won’t be growing as fast as hyperscalers. So, it creates the illusion of shrinkage when it’s actually just slower growth. In fact, after a sustained period of essentially no growth, on-premises data center capacity is receiving a boost thanks to genAI applications and GPU infrastructure. “While most enterprise workloads are gravitating towards cloud providers or to off-premise colo facilities, a substantial subset are staying on-premise, driving a substantial increase in enterprise GPU servers,” said John Dinsdale, a chief analyst at Synergy Research Group.

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He pointed out that, in addition to its continued growth, OCI has a remaining performance obligation (RPO) — total future revenue expected from contracts not yet reported as revenue — of $138 billion, a 41% increase, year over year. The company is benefiting from the immense demand for cloud computing largely driven by AI models. While traditionally an enterprise resource planning (ERP) company, Oracle launched OCI in 2016 and has been strategically investing in AI and data center infrastructure that can support gigawatts of capacity. Notably, it is a partner in the $500 billion SoftBank-backed Stargate project, along with OpenAI, Arm, Microsoft, and Nvidia, that will build out data center infrastructure in the US. Along with that, the company is reportedly spending about $40 billion on Nvidia chips for a massive new data center in Abilene, Texas, that will serve as Stargate’s first location in the country. Further, the company has signaled its plans to significantly increase its investment in Abu Dhabi to grow out its cloud and AI offerings in the UAE; has partnered with IBM to advance agentic AI; has launched more than 50 genAI use cases with Cohere; and is a key provider for ByteDance, which has said it plans to invest $20 billion in global cloud infrastructure this year, notably in Johor, Malaysia. Ellison’s plan: dominate the cloud world CTO and co-founder Larry Ellison announced in a recent earnings call Oracle’s intent to become No. 1 in cloud databases, cloud applications, and the construction and operation of cloud data centers. He said Oracle is uniquely positioned because it has so much enterprise data stored in its databases. He also highlighted the company’s flexible multi-cloud strategy and said that the latest version of its database, Oracle 23ai, is specifically tailored to the needs of AI workloads. Oracle

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Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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