NUESTRO BLOG

Introduction The open-source AI ecosystem reached a turning point in August 2025 when Elon Musk’s company xAI released Grok 2.5 and, almost simultaneously, OpenAI launched two new models under the names GPT-OSS-20B and GPT-OSS-120B. While both announcements signalled a commitment to transparency and broader accessibility, the details of these releases highlight strikingly different approaches to what open AI should mean. This article explores the architecture, accessibility, performance benchmarks, regulatory compliance and wider industry impact of these three models. The aim is to clarify whether xAI’s Grok or OpenAI’s GPT-OSS family currently offers more value for developers, businesses and regulators in Europe and beyond. What Was Released Grok 2.5, described by xAI as a 270 billion parameter model, was made available through the release of its weights and tokenizer. These files amount to roughly half a terabyte and were published on Hugging Face. Yet the release lacks critical elements such as training code, detailed architectural notes or dataset documentation. Most importantly, Grok 2.5 comes with a bespoke licence drafted by xAI that has not yet been clearly scrutinised by legal or open-source communities. Analysts have noted that its terms could be revocable or carry restrictions that prevent the model from being considered genuinely open source. Elon Musk promised on social media that Grok 3 would be published in the same manner within six months, suggesting this is just the beginning of a broader strategy by xAI to join the open-source race. By contrast, OpenAI unveiled GPT-OSS-20B and GPT-OSS-120B on 5 August 2025 with a far more comprehensive package. The models were released under the widely recognised Apache 2.0 licence, which is permissive, business-friendly and in line with requirements of the European Union’s AI Act. OpenAI did not only share the weights but also architectural details, training methodology, evaluation benchmarks, code samples and usage guidelines. This represents one of the most transparent releases ever made by the company, which historically faced criticism for keeping its frontier models proprietary. Architectural Approach The architectural differences between these models reveal much about their intended use. Grok 2.5 is a dense transformer with all 270 billion parameters engaged in computation. Without detailed documentation, it is unclear how efficiently it handles scaling or what kinds of attention mechanisms are employed. Meanwhile, GPT-OSS-20B and GPT-OSS-120B make use of a Mixture-of-Experts design. In practice this means that although the models contain 21 and 117 billion parameters respectively, only a small subset of those parameters are activated for each token. GPT-OSS-20B activates 3.6 billion and GPT-OSS-120B activates just over 5 billion. This architecture leads to far greater efficiency, allowing the smaller of the two to run comfortably on devices with only 16 gigabytes of memory, including Snapdragon laptops and consumer-grade graphics cards. The larger model requires 80 gigabytes of GPU memory, placing it in the range of high-end professional hardware, yet still far more efficient than a dense model of similar size. This is a deliberate choice by OpenAI to ensure that open-weight models are not only theoretically available but practically usable. Documentation and Transparency The difference in documentation further separates the two releases. OpenAI’s GPT-OSS models include explanations of their sparse attention layers, grouped multi-query attention, and support for extended context lengths up to 128,000 tokens. These details allow independent researchers to understand, test and even modify the architecture. By contrast, Grok 2.5 offers little more than its weight files and tokenizer, making it effectively a black box. From a developer’s perspective this is crucial: having access to weights without knowing how the system was trained or structured limits reproducibility and hinders adaptation. Transparency also affects regulatory compliance and community trust, making OpenAI’s approach significantly more robust. Performance and Benchmarks Benchmark performance is another area where GPT-OSS models shine. According to OpenAI’s technical documentation and independent testing, GPT-OSS-120B rivals or exceeds the reasoning ability of the company’s o4-mini model, while GPT-OSS-20B achieves parity with the o3-mini. On benchmarks such as MMLU, Codeforces, HealthBench and the AIME mathematics tests from 2024 and 2025, the models perform strongly, especially considering their efficient architecture. GPT-OSS-20B in particular impressed researchers by outperforming much larger competitors such as Qwen3-32B on certain coding and reasoning tasks, despite using less energy and memory. Academic studies published on arXiv in August 2025 highlighted that the model achieved nearly 32 per cent higher throughput and more than 25 per cent lower energy consumption per 1,000 tokens than rival models. Interestingly, one paper noted that GPT-OSS-20B outperformed its larger sibling GPT-OSS-120B on some human evaluation benchmarks, suggesting that sparse scaling does not always correlate linearly with capability. In terms of safety and robustness, the GPT-OSS models again appear carefully designed. They perform comparably to o4-mini on jailbreak resistance and bias testing, though they display higher hallucination rates in simple factual question-answering tasks. This transparency allows researchers to target weaknesses directly, which is part of the value of an open-weight release. Grok 2.5, however, lacks publicly available benchmarks altogether. Without independent testing, its actual capabilities remain uncertain, leaving the community with only Musk’s promotional statements to go by. Regulatory Compliance Regulatory compliance is a particularly important issue for organisations in Europe under the EU AI Act. The legislation requires general-purpose AI models to be released under genuinely open licences, accompanied by detailed technical documentation, information on training and testing datasets, and usage reporting. For models that exceed systemic risk thresholds, such as those trained with more than 10²⁵ floating point operations, further obligations apply, including risk assessment and registration. Grok 2.5, by virtue of its vague licence and lack of documentation, appears non-compliant on several counts. Unless xAI publishes more details or adapts its licensing, European businesses may find it difficult or legally risky to adopt Grok in their workflows. GPT-OSS-20B and 120B, by contrast, seem carefully aligned with the requirements of the AI Act. Their Apache 2.0 licence is recognised under the Act, their documentation meets transparency demands, and OpenAI has signalled a commitment to provide usage reporting. From a regulatory standpoint, OpenAI’s releases are safer bets for integration within the UK and EU. Community Reception The reception from the AI community reflects these differences. Developers welcomed OpenAI’s move as a long-awaited recognition of the open-source movement, especially after years of criticism that the company had become overly protective of its models. Some users, however, expressed frustration with the mixture-of-experts design, reporting that it can lead to repetitive tool-calling behaviours and less engaging conversational output. Yet most acknowledged that for tasks requiring structured reasoning, coding or mathematical precision, the GPT-OSS family performs exceptionally well. Grok 2.5’s release was greeted with more scepticism. While some praised Musk for at least releasing weights, others argued that without a proper licence or documentation it was little more than a symbolic gesture designed to signal openness while avoiding true transparency. Strategic Implications The strategic motivations behind these releases are also worth considering. For xAI, releasing Grok 2.5 may be less about immediate usability and more about positioning in the competitive AI landscape, particularly against Chinese developers and American rivals. For OpenAI, the move appears to be a balancing act: maintaining leadership in proprietary frontier models like GPT-5 while offering credible open-weight alternatives that address regulatory scrutiny and community pressure. This dual strategy could prove effective, enabling the company to dominate both commercial and open-source markets. Conclusion Ultimately, the comparison between Grok 2.5 and GPT-OSS-20B and 120B is not merely technical but philosophical. xAI’s release demonstrates a willingness to participate in the open-source movement but stops short of true openness. OpenAI, on the other hand, has set a new standard for what open-weight releases should look like in 2025: efficient architectures, extensive documentation, clear licensing, strong benchmark performance and regulatory compliance. For European businesses and policymakers evaluating open-source AI options, GPT-OSS currently represents the more practical, compliant and capable choice.  In conclusion, while both xAI and OpenAI contributed to the momentum of open-source AI in August 2025, the details reveal that not all openness is created equal. Grok 2.5 stands as an important symbolic release, but OpenAI’s GPT-OSS family sets the benchmark for practical usability, compliance with the EU AI Act, and genuine transparency.

Real-time communication powers the modern digital world, from virtual healthcare and remote education to online gaming and live broadcasting. Today’s users expect seamless, low-latency, high-quality media experiences across devices. For developers building these applications, selecting the right communication infrastructure is critical. That’s where LiveKit comes in. LiveKit is an open-source platform that provides the building blocks for real-time audio and video apps . With support for WebRTC , AI integrations , and flexible deployment options (cloud or self-hosted), LiveKit helps developers build scalable, production-grade communication tools that meet the highest standards for performance and privacy. What Is LiveKit? LiveKit is a real-time communication framework designed to simplify the creation of interactive audio and video applications. Built on WebRTC, LiveKit provides: A robust media server Client SDKs for multiple platforms End-to-end media encryption AI integration support Cloud and self-hosted deployment models Unlike many closed, vendor-locked platforms, LiveKit’s open-source model gives developers complete control over infrastructure and customisation, allowing for tailored experiences across industries. How Does LiveKit Work? At the core of LiveKit is an SFU (Selective Forwarding Unit), which efficiently routes video and audio streams without decoding or re-encoding them. This keeps latency low and CPU usage minimal, especially during group calls or interactive broadcasts. LiveKit’s architecture includes: Client SDKs : Available for JavaScript, iOS (Swift), Android (Kotlin), Flutter, Unity, React Native, Node.js, Go, and Rust. Room and Participant Management : APIs to create, manage, and moderate rooms and users. Track-Level Controls : Fine-grained control to mute, pause, or adjust specific streams dynamically. Webhooks and Events : For integration with third-party tools like analytics, moderation, or AI services. Recording & Streaming : Capture sessions and broadcast them to platforms like YouTube, Twitch, or private servers. Deployment Options: Cloud or Self-Hosted LiveKit offers two deployment options to suit different levels of control and scalability: 1. Self-Hosted Deploy on your own infrastructure using Kubernetes, Docker, or custom configurations. This is ideal for teams with: Strict compliance requirements (HIPAA, GDPR, etc.) Specific scaling strategies Custom AI workflows Full control over data handling 2. LiveKit Cloud A fully managed service hosted by LiveKit. It offers: Instant setup Automatic scaling Usage-based pricing A generous free tier Perfect for startups, MVPs, and teams that want fast time-to-market without managing servers. Scalability and Performance LiveKit was designed for high performance under demanding workloads: End-to-End Encryption (E2EE) : Ensures complete privacy, even LiveKit itself can't access media. Global Scalability : Redis + Kubernetes allow for horizontal scaling across regions. Bandwidth Optimisation : LiveKit dynamically adjusts media quality based on device and network conditions. Adaptive Layers : For video resolution and bitrate to match various screen sizes and connectivity. Stability Under Load : Real-time systems need to be reliable. LiveKit supports thousands of concurrent users with minimal latency and consistent quality. AI-Powered Real-Time Applications One of LiveKit’s unique strengths is its support for real-time AI use cases. Developers can route media streams directly into AI engines for processing and feedback. Popular AI integrations include: Live transcription using Whisper, Google Cloud Speech, or Deep gram Real-time translation and captioning for multilingual audiences Voice-based commands and virtual assistants AI tutors and agents in educational or enterprise platforms Emotion detection and intent classification in support calls Call summarisation and analytics for sales or support. These features allow you to create smarter, more responsive real-time applications. Use Cases Across Industries LiveKit is versatile and can be used in nearly any vertical. Common use cases include: Education : Virtual classrooms, language learning apps, breakout rooms Healthcare : Telehealth consultations, remote diagnostics, patient engagement tools Gaming : In-game team chat, spatial audio, live streams Enterprise : Internal communication tools, secure video conferencing Media & Events : Live webinars, conferences, hybrid events Customer Support : AI-assisted video support desks with chat and analytics Because LiveKit is fully customizable and open-source, developers can adapt it to match specific workflows, branding, or compliance frameworks. Why Developers Choose LiveKit Compared to other real-time video platforms, LiveKit offers: Open Source Freedom : No lock-in, transparent development Extensive SDK Support : Develop once, deploy anywhere Customisable Infrastructure : Tailor the experience end-to-end Self-Hosting Options : For privacy and control Ready for AI : Integrate voice, vision, and NLP models natively Active Developer Community : Ongoing improvements, documentation, and support Whether you’re building an internal collaboration suite or a consumer-facing live streaming platform, LiveKit gives you the tools and flexibility needed to deliver a standout experience. Pricing and Plans LiveKit offers a tiered pricing model to support projects at every stage: Free Tier : Up to 100 participants, 5,000 connection minutes, 50 GB bandwidth Ship Plan ($50/month) : For growing apps with 150,000 minutes and more bandwidth Scale Plan ($500/month) : Suitable for production apps and large user bases Enterprise Plan : Custom support, SLAs, dedicated infrastructure You can also self-host LiveKit for free, which is ideal for teams that prefer full control. Frequently Asked Questions (FAQ) Is LiveKit really free? Yes. The core LiveKit project is open-source and can be self-hosted at no cost. For managed services, there's a free tier to get started. What makes LiveKit different from Twilio or Agora? LiveKit offers full infrastructure control through open-source code, making it ideal for teams that want to customise everything. Twilio and Agora are fully managed but offer less flexibility. Can LiveKit be used with AI models? Absolutely. LiveKit is designed to route audio and video directly into AI services—whether for transcription, translation, summarisation, or emotion analysis. Is it secure for healthcare or financial apps? Yes. LiveKit supports end-to-end encryption, fine-grained access control, and can be deployed in compliance-friendly environments. How many users can LiveKit handle? Thousands. Its horizontally scalable architecture allows you to grow capacity as your audience expands. Conclusion: The Future of Real-Time Communication In a world increasingly driven by real-time interaction and AI-enhanced communication, LiveKit is positioned as a top-tier solution for developers. Its open-source nature, scalable infrastructure, and flexibility make it ideal for any team that wants to build custom, high-performance communication applications. Whether you're launching a telehealth app, a virtual classroom, or a next-gen support platform, LiveKit gives you everything you need, from WebRTC handling to AI-ready pipelines, in one powerful framework.

Introduction Runner H is a groundbreaking product recently launched by H Company, designed to redefine how artificial intelligence interacts with the internet. More than just a software tool, Runner H is a complete autonomous web browsing agent. Paired with the open-source Surfer H framework and the Hollow One family of vision-language models, it is setting a new standard for intelligent automation. This article explores the mechanics behind this innovation, its real-world applications, and the reasons it is being described as a breakthrough in browser-based automation. The project is particularly notable for its open-source approach and the high level of transparency in its technical documentation and research methodology. What is Runner H and How Does it work Runner H functions as a digital assistant that receives a task in natural language and executes it independently. Users can request operations such as searching for active listings on eBay, compiling information into a Google Sheet, or navigating complex websites. The agent follows instructions by visually interpreting websites just as a human would, performing clicks, scrolling, typing, and even validating the results of its actions. It can work across several tasks simultaneously, which opens the door to impressive levels of productivity in both consumer and enterprise use cases. Surfer H: The Open Source Framework At the heart of this system is Surfer H, a framework built to empower agents to operate with human-like behaviour in web environments. It does not rely on APIs, structured data access, or pre-defined site integrations. Instead, the system uses screenshots to understand the interface and generate a plan of action. The architecture is composed of three essential components. The Policy module determines what actions should be taken to complete the task. The Localiser identifies where on the screen to interact by predicting coordinates based on the content of screenshots. Finally, the Validator reviews the outcome of each action and confirms whether the task was completed correctly. If not, it feeds back that information and the agent iterates accordingly, refining its strategy until the goal is met or the cost or time budget is reached. Hollow One Models: Small, Fast and Open The models behind this system belong to the Hollow One family. These are compact, efficient vision-language models specifically designed for interpreting web interfaces. Available openly on Hugging Face, they are trained to understand UI images and respond to tasks such as “Book a hotel in Paris for three nights in August.” The model generates a navigation sequence, identifies the relevant parts of the screen, and proceeds as if it were a user. This approach works regardless of the underlying code or document structure of the website. It means the agent can navigate any site as long as it can see it. Developers can even download, fine-tune, and redeploy these models for custom use cases, ensuring full flexibility and long-term viability. Research Highlights: How Surfer H Works The research paper released by H Company provides a detailed explanation of the system. It presents Surfer H as a visual web retrieval agent trained through reinforcement learning techniques. The agent does not use the document object model or accessibility trees but instead relies entirely on visual input and learned behaviour. The three-part system is tightly integrated. The Policy module suggests what to do. The Localiser figures out where to do it. The Validator confirms if it was done correctly. If the result is unsatisfactory, the system updates its memory and revises its actions. This loop continues until the task is completed or a cost or time limit is reached. What makes this setup so powerful is that it mirrors human decision-making without depending on brittle or hard-coded logic. Benchmark Results and Performance In performance tests, particularly the Web Voyager benchmark, Runner H, powered by Hollow One, achieved a state-of-the-art success rate of 92.2 per cent. It also demonstrated exceptional cost efficiency. For example, while using the 7 billion parameter version of Hollow One, each task cost only 13 cents. In contrast, using Surfer H with GPT-4 yielded a lower success rate at a significantly higher cost per task. These results highlight the strategic balance between accuracy and computational cost that Hollow One achieves. Charts in the official documentation demonstrate that Hollow One models consistently outperform their peers in both precision and economic value, making them an ideal choice for real-world deployment. Why This Matters: Real-World Use Cases One of the most powerful demonstrations of Runner H is its ability to handle tasks in parallel. Multiple agents can be launched simultaneously, each pursuing a different task. This level of scalability makes it suitable for enterprise automation scenarios, such as data extraction, lead generation, content monitoring, or customer service integration. For small teams and startups, the open-source nature of the tool allows for experimentation and implementation without major upfront costs, levelling the playing field for innovation. Integrations, Autonomy, and Control Additionally, Runner H includes native support for popular platforms and services. It can integrate with Google Docs, Google Sheets, Notion, Slack, and Zapier. Users can upload files as context, connect their accounts for seamless access, and even set up autonomous workflows that involve document generation or API interaction. Soon, payment capabilities will also be introduced, allowing agents to perform online purchases or transactions on behalf of users. Another feature worth noting is the customisation of automation depth, letting users choose between high human involvement or full autonomy depending on the use case and level of trust in the agent’s ability. Tester H: Automated Web QA is Coming H Company has also introduced Tester H, a related product currently in private beta. It enables fully automated quality assurance testing for websites and applications. By writing natural language test scenarios like “Open Airbnb and confirm the image of the orange bed appears,” users can automate UI testing without scripting. This complements the broader ecosystem being built around intelligent browser agents. Once Tester H is fully released, it could revolutionise the way software development teams validate their releases and deploy code safely. A General and Scalable Solution  What truly differentiates Runner H and Surfer H from other automation platforms is their generality. Most browser automation tools depend on specific integrations or developer-maintained scripts. Runner H requires none of that. It is capable of understanding, reasoning, and interacting with any interface it can see. This makes it ideal for environments where APIs do not exist or where flexibility and adaptability are paramount. Because the system is model-agnostic, it can be paired with various large language models and vision systems, giving organisations the ability to choose the right balance of speed, price, and performance. Conclusion In summary, H Company’s release of Runner H and Surfer H is a major milestone in the evolution of web automation. By combining advanced vision-language models with an intuitive and robust framework, they have built an open system that performs human-level web tasks at scale, with minimal setup and outstanding accuracy. Whether used in research, industry, or everyday productivity, Runner H represents the next phase of digital agents. It is autonomous, intelligent, efficient, and fully open to the world. This is not only a technical achievement but a signal that the future of AI-driven human-computer interaction is more accessible and flexible than ever before.

Introduction: A New Era in Artificial Intelligence In May 2025, Google DeepMind introduced a ground-breaking innovation poised to transform how artificial intelligence is developed, optimised, and deployed. Known as Alpha Evolve, this evolutionary AI system represents a significant shift from passive machine learning models to autonomous, agentic AI, capable of iteratively improving code without human intervention. Alpha Evolve doesn't merely assist with programming—it generates new code, tests variants, evaluates performance, and refines results through a self-directed loop. Built upon the powerful Gemini multimodal model, Alpha Evolve exemplifies the next frontier in AI development: systems that actively evolve and improve their algorithms to achieve greater efficiency and effectiveness. What Is Alpha Evolve? Alpha Evolve is an agentic AI system designed to discover, test, and optimise algorithms in a fully autonomous manner. Unlike traditional AI models that passively await user prompts or require human-supervised training, Alpha Evolve takes initiative. It explores vast solution spaces, retains lessons from previous iterations, and continually refines its output in pursuit of performance goals. At its core, Alpha Evolve performs the following: Generates code variants via intelligent prompt sampling. Evaluates performance based on predefined metrics like speed, efficiency, and energy use. Retains and learns from each attempt using a dynamic program memory. Iterates autonomously , improving algorithms over time with minimal human oversight. This feedback loop allows the AI to tackle complex computational challenges, optimise system performance, and even make scientific discoveries, all without relying on step-by-step human guidance. How Does Alpha Evolve Work? Alpha Evolve follows a cyclical agentic architecture, similar to an evolutionary algorithm. Here's how it operates: Initial Input: The system is given a coding task or optimisation goal (e.g., reduce energy use in a TPU circuit). Code Generation: It creates numerous candidate solutions by mutating existing code or generating new implementations from scratch. Evaluation: Each version is tested through simulations or real-world benchmarks, receiving a performance score. Selection and Retention: The top-performing code is retained and used as a baseline for the next generation. Iteration: This process repeats, refining results with each cycle. Importantly, Alpha Evolve maintains a programme database of prior attempts, which helps prevent redundancy and accelerates convergence on optimal solutions. Real-World Results at Google Alpha Evolve is already making a tangible impact across Google’s infrastructure and AI research pipeline. Here are four significant achievements to date: 1. Optimised Job Scheduling in Google Data Centres By applying Alpha Evolve to the Borg scheduler—Google's job allocation system—engineers recovered 0.7% of compute resources. While this may seem modest, across Google's immense server network, such savings represent millions in cost reductions and substantial energy efficiency gains. 2. Improved TPU Circuit Designs Alpha Evolve was used to re-engineer circuits in Google’s Tensor Processing Units (TPUs). The AI discovered ways to remove redundant components, resulting in: Lower power consumption Reduced heat generation Fewer operational errors This marks a rare example of AI contributing directly to hardware optimisation, not just software efficiency. 3. Faster Gemini Model Training Training large-scale AI models, such as Gemini, is computationally intensive. Alpha Evolve significantly improved kernel-level tiling heuristics for matrix multiplication—a critical operation in model training. Result: 23% faster execution on key kernels Impact: 1% total reduction in training time for Gemini Such improvements compound at scale, saving millions of GPU hours and accelerating development cycles across teams. 4. Broke a 56-Year-Old Matrix Multiplication Record In a stunning demonstration of scientific discovery, Alpha Evolve found a novel way to multiply 4×4 matrices using fewer operations than the classic Strassen algorithm (1969). This breakthrough has significant implications for: Theoretical computer science Graphics rendering Deep learning computation This achievement illustrates how AI systems are now capable of making original contributions to mathematics—a task once thought to require human creativity. The Road to Recursive Self-Improvement One of the most exciting possibilities introduced by Alpha Evolve is the concept of recursive self-improvement. By integrating its optimisation outputs back into base AI models like Gemini, DeepMind may initiate a loop where AI systems continually enhance themselves, refining not only task performance but their own training and development frameworks. While still speculative, this pathway could usher in: Exponential increases in AI capability Shorter iteration cycles Accelerated scientific discovery This feedback mechanism may even lay the groundwork for artificial general intelligence (AGI)—a milestone that could redefine the role of AI in human society. Automating the Entire Research Pipeline Looking beyond code optimisation, Google envisions Alpha Evolve and future agents automating nearly all aspects of AI research: Literature review: Reading and summarising vast academic corpora Hypothesis generation: Formulating testable ideas Experimental design: Structuring trials and simulations Analysis and interpretation: Drawing conclusions and suggesting next steps This end-to-end automation could compress decades of progress into a few months, allowing AI systems to solve problems that are currently intractable due to time and resource constraints. According to internal researchers, such capabilities may become a reality before 2030. Human-AI Collaboration Remains Crucial Despite Alpha Evolve’s autonomy, human involvement remains vital. Human oversight enhances: Exploration boundaries: Guiding the AI toward meaningful solution spaces Ethical safeguards : Preventing misuse or unintended outcomes Creative integration: Combining machine-discovered insights with human intuition Far from replacing developers and researchers, Alpha Evolve is best viewed as an amplifier of human ingenuity, allowing professionals to focus on high-level strategy while the AI handles low-level optimisation. Conclusion: The Future Is Evolutive Alpha Evolve is not just an AI model—it’s a new class of intelligent agent, one capable of advancing science and technology without constant human prompting. By automating code refinement, accelerating hardware design, and contributing original discoveries, Alpha Evolve sets a precedent for what AI can become. The implications are profound: Businesses can optimise infrastructure at scale. Scientists can test theories in days instead of years. AI systems can continually learn and improve themselves. In short, Alpha Evolve evolves AI itself. As we look toward a future of recursive self-improvement and automated research, one thing is clear: agentic AI is here, and it’s changing everything. Frequently Asked Questions (FAQs) 🔹 What makes Alpha Evolve different from other AI systems? Unlike traditional AI models that require human input for every iteration, Alpha Evolve acts independently. It generates, evaluates, and refines code through autonomous feedback loops. 🔹 Is Alpha Evolve available to the public? Currently, Alpha Evolve is used internally at Google. However, DeepMind has suggested future limited access for academic and trusted researchers. 🔹 Could Alpha Evolve replace human software engineers? Not entirely. While Alpha Evolve handles routine and complex optimisation tasks, human guidance and creativity remain essential for setting goals, interpreting results, and ensuring the ethical use of AI.

In the ever-evolving landscape of artificial intelligence, Google has recently introduced Agent Space. This revolutionary platform is set to redefine how businesses and developers create and deploy AI agents. By introducing the Agent-to-Agent (A2A) protocol, Google is making it easier for AI agents to communicate seamlessly across different platforms, marking a transformative shift in how tasks are performed and automated. But what exactly does Agent Space bring to the table? How will this new technology impact industries, businesses, and everyday users? Let’s dive into this groundbreaking platform and explore its potential for reshaping the AI landscape. What is Agent Space? Google's Agent Space is a platform designed to streamline the process of creating and deploying AI agents that can perform various tasks, ranging from handling customer service inquiries to conducting complex data analysis. These AI agents can work independently or, more interestingly, collaborate through the A2A protocol. The ability for agents to communicate and share information across different platforms marks a huge step forward in terms of interoperability, providing businesses with unprecedented flexibility and efficiency. A key feature of Agent Space is its user-friendly interface, which allows individuals and companies to create custom AI agents without needing any coding expertise. Users can select from a gallery of pre-built agents or develop their own, using simple conversational inputs to define the tasks they want the agent to perform. This ease of use democratises AI development, allowing companies of all sizes to leverage AI without investing heavily in specialised technical resources. The A2A Protocol: A Game Changer in Collaboration At the core of Agent Space's capabilities is the Agent-to-Agent (A2A) protocol, which enables AI agents developed by different providers to communicate securely and efficiently. This protocol ensures that agents can share data, exchange commands, and collaborate on tasks without compatibility issues, all while maintaining strict security and access controls. The A2A protocol is designed to unlock new productivity levels, particularly for businesses that rely on multiple tools and services. For example, a marketing agent could seamlessly work with a finance agent to create a comprehensive report, pulling data from internal systems and external sources. The ability for agents to communicate across platforms, combined with real-time data access, significantly enhances workflow efficiency, eliminating the need for manual data entry or switching between different tools. How Does Agent Space Improve Business Productivity? One of the most exciting benefits of Google’s Agent Space is its ability to boost business productivity. AI agents can automate repetitive tasks, such as scheduling meetings, updating customer records, or generating reports, allowing employees to focus on more strategic activities. The ability for agents to pull real-time data from various internal and external sources means that they can make informed decisions and recommendations without constant oversight. For instance, imagine a scenario where an AI agent monitors a client’s investment portfolio. The agent can access real-time market data, analyse risk factors, and send recommendations to the portfolio manager without human intervention. This kind of automation saves time and ensures that tasks are completed with greater accuracy and efficiency. Moreover, the interoperability offered by the A2A protocol opens up new possibilities for collaboration. Companies can integrate their AI agents into their existing systems, enabling them to work together in a way that was previously impossible. Whether customer service agents collaborate with sales agents to resolve inquiries or supply chain agents work with financial systems to forecast demand, the potential for increased productivity is limitless. The Developer Ecosystem: Empowering Innovation Another significant aspect of Agent Space is its Developer Ecosystem. Google has introduced the Agent Development Kit (ADK), which empowers developers to create and monetise their own AI agents within the platform. This opens up new opportunities for businesses to build niche, custom agents that cater to specific needs within their industry. They also provide developers with a way to earn revenue by offering their agents to other companies. The ADK is designed to be accessible to developers of all skill levels, with comprehensive documentation and support to guide them through the development process. Whether you're building a simple automation tool or a complex agent capable of handling multiple tasks across different sectors, the ADK provides the resources you need to get started. This developer-first approach ensures that Agent Space will continue to evolve and expand as more innovative agents are created, further enhancing the platform’s capabilities. Over time, we may even see the emergence of a marketplace where businesses can source agents based on their needs, similar to how apps are distributed through app stores today. Security and Privacy: A Priority in Agent Space As with any platform that handles sensitive data, security is a top priority in Agent Space. The platform incorporates enterprise-grade authentication and user-level access controls, ensuring that data privacy and security are never compromised. With companies increasingly relying on AI to manage sensitive information, robust security measures are essential. The ability to restrict access to specific data or tasks based on user roles allows businesses to maintain tight control over their operations. Whether dealing with customer data, financial records, or confidential communications, Agent Space ensures that only authorised users and agents have access to specific information. The Future of AI and Search: Moving Beyond Traditional Methods Agent Space also has the potential to transform the way we interact with information on the internet. Traditionally, users have relied on search engines to find information, but Google’s Agent Space offers an alternative approach: AI agents acting on users' behalf. Rather than manually searching for answers, users can interact with AI agents, who will pull relevant information from various sources, make decisions, and complete tasks on their behalf. This shift from search engines to agent-based interactions could fundamentally change how we search for and interact with information. The growing role of AI in our daily lives means that soon, users may not need to visit websites or manually browse through search results to find what they’re looking for. Instead, AI agents will streamline the process, providing users with exactly what they need in real time. Scaling AI Across Industries: The Enterprise Advantage One of the most compelling aspects of Google’s Agent Space is its adaptability across industries of all sizes and complexities. Enterprises can leverage agents to automate everything from supply chain optimisation to regulatory reporting. For instance, in the logistics sector, agents can track shipments in real time, update dashboards dynamically, and communicate with customs agents—all without human intervention. In education, custom-built agents could serve as intelligent tutors, providing personalised feedback, scheduling lessons, and adapting learning paths to individual student needs. For legal firms, agents might review contracts for compliance risks, flag outdated clauses, and prepare summary reports, significantly reducing overhead and turnaround time. This cross-sector flexibility is what makes Agent Space such a transformative platform. It’s not just a tool for tech-savvy startups—it’s an infrastructure layer that can scale with the ambitions of any organisation, whether a multinational enterprise or a growing non-profit. Conclusion: Building the AI Ecosystem of Tomorrow Google’s Agent Space represents more than a step forward—it marks a strategic leap into the era of intelligent collaboration. Blending secure interoperability with intuitive design and developer flexibility creates fertile ground for innovation and growth. Whether you’re a business leader seeking productivity gains, a developer aiming to monetise niche capabilities, or an end-user curious about automation, Agent Space opens the door to a brighter, more connected future. As adoption grows and more agents populate the ecosystem, we may see a profound shift—not just in how we work but in how we think about digital interaction itself. This is the beginning of a human–agent partnership model that puts users at the centre of the AI revolution.