Firefox Project Nova redesign brings compact mode and new look


TL;DR

Mozilla unveiled Project Nova, Firefox’s biggest redesign in six years. It brings softer tabs, a fire-inspired colour palette, compact mode, and clearer privacy controls. The rollout is expected later this year.

Mozilla has officially unveiled Project Nova, the largest visual overhaul of Firefox since 2020. The redesign touches tabs, icons, spacing, colour palette, and settings, with the goal of making the browser feel warmer and faster without losing its identity as the only major browser not built on Chromium.

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The changes start with the tabs. They now have a softer, more rounded shape with a subtle gradient that gives the active tab more visual weight. The rest of the interface follows suit: panels, menus, and browser controls share consistent curves and spacing. Icons have been redrawn for better balance across light and dark themes.

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The colour palette is new too. Mozilla describes it as inspired by fire, with deep smoky purples and lighter warm tones replacing the flatter hues of the current design. The active tab gets a glow effect that ties the whole interface together.

Compact mode is returning. Mozilla removed the option years ago and users have been asking for it back ever since. The reinstated mode condenses browser controls to reclaim vertical screen space, a straightforward concession to the power users who make up a disproportionate share of Firefox’s base.

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Beyond aesthetics, Nova makes privacy tools more visible. The built-in VPN, which Mozilla launched as a free feature with 50 gigabytes of monthly data, gets a more prominent placement. Settings are being rewritten in plainer language, with clearer controls for Enhanced Tracking Protection and the option to turn off AI features entirely.

Mozilla claims Firefox has improved load times for key page content by 9 per cent over the past year. Part of that comes from tracker blocking, which reduces the amount of third-party code a page needs to load. The browser also now prioritises the most important page elements before loading peripheral content.

The redesign extends to mobile. Shared colours, icons, and design tokens will make Firefox feel more consistent across desktop and phone. Mozilla is also adding new themes and wallpapers, with plans to let users customise the shape of interface elements like tabs and components over time.

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Under the hood, Nova introduces a shared design system built on reusable tokens and components. The idea is that future features integrate into a cohesive visual language rather than looking bolted on. That kind of infrastructure work rarely excites users, but it determines how quickly a browser can evolve.

The timing matters. Firefox holds roughly 2.3 per cent of the global browser market, down from double digits a decade ago. Google has been turning Chrome into an AI workplace platform, while also facing scrutiny over its tracking practices. Apple’s Safari holds second place at around 15 per cent. Firefox’s pitch, that it is built for users rather than platforms, needs a modern interface to match.

Mozilla has also been investing in AI on its own terms. Firefox 150 shipped with 271 vulnerability fixes found by Anthropic’s Claude, and the browser now offers optional AI features with a kill switch for users who want none of it. That approach, AI as a choice rather than a default, aligns with the broader Nova philosophy.

Project Nova is available for testing in Firefox Nightly builds now. The full rollout is expected later this year. Mozilla is collecting feedback through its Connect forum, staying true to its open-source tradition of building in public.



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ZDNET’s key takeaways

  • Trusted quality data is the backbone of agentic AI.
  • Identifying high-impact workflows to assign to AI agents is key to scaling adoption.
  • Scaling agentic AI starts with rethinking how work gets done. 

Gartner forecasts that worldwide AI spending will total $2.5 trillion in 2026, a 44% year-over-year increase. Spending on AI platforms for data science and machine learning will reach $31 billion, and spending on AI data will reach $3 billion.

The global agentic AI market will reach $8.5 billion by the end of 2026 and nearly $40 billion by 2030, per Deloitte Digital. Organizations are rapidly accelerating their adoption of AI agents, with the current average utilization standing at 12 agents per organization, according to MuleSoft 2026 research. This rate is projected to increase by 67% over the next two years, reaching an average of 20 AI agents. 

Also: How to build better AI agents for your business – without creating trust issues

According to IDC, by 2026, 40% of all Global 2000 job roles will involve working with AI agents, redefining long-held traditional entry, mid, and senior level positions. But the journey will not be smooth. By 2027, companies that do not prioritize high-quality, AI-ready data will struggle to scale generative AI and agentic solutions, resulting in a 15% loss in productivity. While 2025 was the year of pilot experiments and small production deployments of agentic AI, 2026 is shaping up to be the year of scaling agentic AI. And to scale agentic AI, according to IDC’s forecast, companies will need trustworthy, accessible, and quality data. 

Scaling agentic AI adoption in business requires a strong data foundation, according to McKinsey research. Businesses can create high-impact workflows by using agents, but to do so, they must modernize their data architecture, improve data quality, and advance their operating models. 

McKinsey found that nearly two-thirds of enterprises worldwide have experimented with agents, but fewer than 10% have scaled them to deliver measurable value. The biggest obstacle to scaling agent adoption is poor data — eight in ten companies cite data limitations as a roadblock to scaling agentic AI. 

Also: AI agents are fast, loose, and out of control, MIT study finds

McKinsey identified the top data limitations as primary constraints that companies face when scaling AI, including: operating model and talent constraints, data limitations, ineffective change management, and tech platform limitations. 

Data is the backbone of agentic AI

Research shows that agentic AI needs a steady flow of high-quality, trusted data to accurately automate complex business workflows. Successful agentic AI also depends on a data architecture that can support autonomy — executing tasks without human intervention. 

Two agentic usage models are emerging: single-agent workflows (one agent using multiple tools) and multi-agent workflows (specialized agents collaborate). In each case, agents will rely on access to high-quality data. Data silos and fragmented data would lead to errors and poor agentic decision-making. 

Four steps for preparing your data 

McKinsey identified four coordinated steps that connect strategy, technology, and people in order to build strong foundational data capabilities. 

Also: Prolonged AI use can be hazardous to your health and work: 4 ways to stay safe

  1. Identify high-impact workflows to ‘agentify’. Focus on highly deterministic, repetitive tasks that deliver value as strong candidates for AI agents. 

  2. Modernize each layer of the data architecture for agents. The focus on modernization should support interoperability, easy access, and governance across systems. The vast majority of business applications do not share data across platforms. According to MuleSoft research, organizations are rapidly adopting autonomous systems. The average enterprise now manages 957 applications — rising to 1,057 for those furthest along in their agentic AI journey. Only 27% of these applications are currently connected, creating a significant challenge for IT leaders aiming to meet their near-term AI implementation goals. 

  3. Ensure that data quality is in place. Businesses must ensure that both structured and unstructured data, as well as agent-generated data, meet consistent standards for accuracy, lineage, and governance. Access to trusted data is a key obstacle. IT teams now spend an average of 36% of their time designing, building, and testing new custom integrations between systems and data. Custom work will not help scale AI adoption. The most significant obstacle to successful AI or AI agent deployment is data quality, cited as the top concern by 25% of organizations. Furthermore, almost all organizations (96%) struggle to use data from across the business for AI initiatives.  

  4. Build an operating and governance model for agentic AI. This is about rethinking how work gets done. Human roles will shift from execution to supervision and orchestration of agent-led workflows. In a hybrid work environment, governance will dictate how agents can operate autonomously in a trustworthy, transparent, and scaled manner. 

The work assigned to AI agents 

McKinsey highlighted the importance of identifying a few critical workflows that would be candidates for AI agents to own. To begin, an end-to-end workflow mapping would help identify opportunities for agentic use. McKinsey found that AI adoption is led by customer service, marketing, knowledge management, and IT. It is important to identify clear metrics that validate impact. Teams should identify the data that can be reused across tasks and workflows.

Also: These companies are actually upskilling their workers for AI – here’s how they do it

McKinsey concludes that having access to high-quality data is a strategic differentiator in the agentic AI era. Because agents will generate enormous amounts of data, data quality, lineage, and standardization will be even more important in the agentic enterprise. And as agentic systems scale, governance becomes the primary level for control. The data foundation will be the competitive advantage in the agentic era. 





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