Latest news with #AgentOps
Business Wire
12 hours ago
- Business
- Business Wire
SUPERWISE® Launches First Open, Enterprise AgentOps Solution for Securely Running Third-Party AI Agents
NASHVILLE, Tenn.--(BUSINESS WIRE)--SUPERWISE®, the leading Enterprise AI Governance and Operations platform, today unveiled a bold advancement in the AI landscape. While much of the industry remains focused on building agents, SUPERWISE is tackling the much more complex - and often underestimated - challenge: how to operate AI agents at scale with full governance, observability, and control. This shift reflects a growing recognition that the true test of AI maturity lies not in development, but in dependable, compliant, and scalable operations. 'Building AI agents is only half the equation. The real challenge is managing them responsibly once they're live—and that's where SUPERWISE excels.' — Russ Blattner, CEO, SUPERWISE® With the launch of its open AgentOps platform, SUPERWISE enables companies to safely deploy agents developed in a variety of proprietary and open-source development platforms. This release emboldens teams to deploy, serve, and manage AI agents within the SUPERWISE platform, complete with built-in compliance, monitoring, and operational oversight. It marks a significant step forward in making responsible AI not just possible, but secure, scalable, and successful. 'Building AI agents is only half the equation,' said Russ Blattner, CEO of SUPERWISE. 'The real challenge, and where organizations often stumble, is in managing them responsibly once they are live. This is precisely where SUPERWISE's expertise and leadership have consistently distinguished us - at the operational layer. With this launch, SUPERWISE is enabling teams to use the best open-source tools to build agents, while relying on our enterprise-grade infrastructure to govern, observe, and scale them safely.' SUPERWISE's AgentOps platform will benefit a variety of stakeholders: AI Developers & Engineers: Use their preferred tools without sacrificing operational oversight Enterprise IT & AI Leaders: Centralize operations while enabling innovation and avoiding vendor lock-in C-Level Executives: Balance agility with governance, security, and scalability, and a lower total cost of ownership The platform enables developers to capitalize on a variety of common preferences, including open-source software, low-code interfaces, built-in integrations, strong community support, and ease-of-deployment. The platform today supports the deployment and operation of agents for its development framework Flowise, and a growing list of soon-to-be announced third-party frameworks, including Dify, CrewAI, Langflow, N8n and many others. 'Developers have their choices for open source frameworks. Rather than forcing them to switch in order to be governed, SUPERWISE allows developed agents to be run in our platform, which maximizes existing development investment without incurring the risks,' said Oren Razon, Senior Director of Product at SUPERWISE. About SUPERWISE SUPERWISE® is the Enterprise AI Governance and Operations Platform purpose-built for real-world AI. With a platform-first approach, it unifies artificial intelligence operations and governance, risk, and compliance into a single, scalable foundation, enabling complex industries to deploy, run, monitor, and manage AI with confidence and proper oversight. Recognized by Gartner as a Cool Vendor in Enterprise AI Governance and a pioneer in MLOps, SUPERWISE® delivers built-in guardrails, observability, explainability, and compliance, positioning AI as a trusted foundation for successful business transformation.
Techday NZ
12 hours ago
- Business
- Techday NZ
Superwise launches AgentOps for secure & compliant AI agent management
SUPERWISE has announced the introduction of its open AgentOps platform designed to provide real-time observability, control, and compliance for companies deploying third-party AI agents. The new solution is intended to address what the company describes as a significant gap in the industry, as businesses ramp up their deployment of AI agents without adequate measures for risk mitigation and operational oversight. The AgentOps platform seeks to centralise and secure the management of AI agents, serving companies that increasingly rely on varied and decentralised agent architectures. Operational oversight The AgentOps release enables enterprises to deploy, serve, and manage AI agents created using a range of proprietary and open-source development platforms. Through this initiative, SUPERWISE provides built-in capabilities for compliance, monitoring, and operational management, positioning its service as a component for responsible and scalable AI deployment. Russ Blattner, Chief Executive Officer at SUPERWISE, highlighted the current challenges in the AI landscape. "Building AI agents is only half the equation," he said. "The real challenge, and where organizations often stumble, is in managing them responsibly once they are live. This is precisely where SUPERWISE's expertise and leadership have consistently distinguished us - at the operational layer. With this launch, SUPERWISE is enabling teams to use the best open-source tools to build agents, while relying on our enterprise-grade infrastructure to govern, observe, and scale them safely." Supporting diverse needs The AgentOps platform is aimed at a broad spectrum of stakeholders within the enterprise. AI developers and engineers are able to continue using their preferred frameworks and tools while maintaining operational visibility and controlled workflows. Enterprise IT and AI leaders are provided with centralised management, allowing them to encourage innovation while avoiding dependency on single vendors. C-level executives are presented with tools to balance agility, governance, security, scalability, and cost. The development philosophy behind AgentOps includes support for open-source software and low-code solutions, as well as built-in integrations and community-driven tooling. According to the company, the platform currently supports the deployment and management of agents developed through its Flowise framework, with planned compatibility for additional third-party frameworks such as Dify, CrewAI, Langflow, and N8n. Framework flexibility Oren Razon, Senior Director of Product at SUPERWISE, commented on the platform's role in letting developers maximise the investment in their tool choices. "Developers have their choices for open source frameworks. Rather than forcing them to switch in order to be governed, SUPERWISE allows developed agents to be run in our platform, which maximizes existing development investment without incurring the risks," he said. SUPERWISE claims that as the deployment of AI agents becomes more widespread, the need for integrated governance, risk management, and operational transparency will increase across industries. The AgentOps platform is positioned to offer enterprises a cohesive and extensible approach to agent oversight, aimed at supporting ongoing compliance requirements and auditability as regulatory frameworks evolve. The company points toward its experience in governance and operations as being central to this new release, making reference to the rising recognition within the industry that maintaining secure and auditable AI systems is becoming as critical as developing the agents themselves. SUPERWISE is recognised by analyst firms for its contributions to enterprise AI governance and MLOps, framing its platform as offering integrated guardrails and compliance functionality for enterprises seeking to embed responsible AI within their operational processes.
Forbes
11-04-2025
- Business
- Forbes
XOps For Enterprise AI: The Convergence Of MLOps, LLMOps And AgentOps
Chiranjiv Roy spearheads globally products, solution and consulting across industries at getty Today's enterprise AI landscape faces exponential growth in model complexity and data volumes, posing significant challenges. As organizations rapidly scale their AI ambitions, they inevitably encounter bottlenecks related to operational efficiency, compliance and scalability. To address these challenges comprehensively, businesses require an integrated approach—XOps, which combines MLOps, LLMOps and AgentOps. This unified framework isn't merely about execution; it's about strategically leveraging AI operations to deliver sustainable business value. AI operations today require more than discrete practices. MLOps helps streamline traditional machine learning workflows, LLMOps enables the efficient deployment of sophisticated language models and AgentOps coordinates complex autonomous agent systems. However, implementing these components in isolation misses significant opportunities for holistic efficiency and strategic value. XOps solves this by bringing these distinct yet complementary operational disciplines under one strategic umbrella, ensuring smoother, more scalable adoption of AI capabilities. For example, in one impactful experience with a global consumer electronics company, supply chains suffered from manual, resource-heavy processes, slowing insights and innovation. By developing a no-code, intuitive ML platform with automated data pipelines and AutoML capabilities, business and data analysts independently designed and deployed models without extensive IT involvement. The results were transformative: • Dramatically faster project cycles • Significantly reduced dependence on engineering teams • Enhanced strategic agility, empowering quicker, informed supply chain decisions As another example, in healthcare, inconsistent and siloed workflows complicate and delay AI adoption due to compliance risks. Establishing a standardized, end-to-end MLOps pipeline ensures consistent, compliant model deployment across diverse teams. In our experience, automating data preprocessing, model validation and real-time monitoring can significantly shorten deployment timelines, improve regulatory compliance and strengthen collaboration between technical and business stakeholders. A digital analytics agency we worked with faced slow insights generation and scalability issues from fragmented NLP processes. Integrating CI/CD pipelines for NLP models on cloud infrastructure accelerated insights and improved model accuracy. Automated data preprocessing and robust governance mechanisms ensured reliable and trustworthy analytics. Business outcomes included: • Analysis time reduced from weeks to near real time • Increased accuracy and reliability of marketing insights • Improved scalability and responsiveness to changing market demands Implementing XOps successfully at an enterprise level requires more than technology and talent—it demands a structured, strategic approach that aligns clearly with business objectives and operational realities. Please keep in mind that both engineering and machine learning/data science teams need to get aligned so that both learn the ways of working. Begin by combining deep data science expertise, domain-specific experience and MLOps proficiency. Assemble cross-functional teams, including data scientists, domain experts and solution architects who champion end-to-end ML lifecycle management and are familiar with industry-specific use cases, particularly in areas like consumer packaged goods (CPG). Move from basic DevOps to full-scale automated MLOps by setting up structured automation stages: • Automated data gathering and version control • Automated training with robust monitoring and model evaluation • CI/CD-driven automated deployment with infrastructure as code (IaC) • Automated retraining to sustain model performance and interpretability Address the critical gaps between model development and operational deployment by ensuring continuous governance, standardized metrics and integrated training processes. Focus not just on sophisticated models but on building reliable, repeatable processes that enable smooth transitions from development to production. Establish comprehensive ML operational excellence by implementing the following: • Version control for traceability • CI/CD pipelines for streamlined deployment • Infrastructure-as-code for reproducible infrastructure • Model monitoring to proactively address degradation • Automated model deployment to minimize manual intervention • Data operations to ensure data traceability and integrity To execute a planned production approach effectively, it is essential to begin with a thorough understanding of the data and models involved. Next, refactor the code to ensure scalability in a production environment. Develop automated pipelines to standardize workflows and maintain consistency. Finally, implement deployment strategies that incorporate seamless monitoring, allowing models to adapt dynamically to real-world conditions. The transition should be smooth from concept to engineering, as this process involves serious change management. To effectively scale an AI application from concept to production, we follow a structured, iterative process encompassing multiple clearly defined stages: • Define: Begin by collaborating closely with business consultants and SMEs to articulate the business questions, objectives and requirements. • Design: Proceed with comprehensive data acquisition and preparation. This stage ensures the data quality is robust and suitable for further modeling. • Describe: Implement feature engineering, model training and experimentation. Evaluate and compare models meticulously to select the optimal approach for deployment. • Deploy: Integrate models into user-centric applications via intuitive UI/UX designs, dashboards or web and mobile applications. The deployment also includes ensuring data schema alignment and leveraging granular-level model optimization using approaches like GraphRAG. • Drive: Continuously monitor and track model performance in production. Incorporate consumer feedback for ongoing model refinement and improvisations, fostering a responsive and adaptive model lifecycle that aligns with real-world performance and consumer expectations. Looking ahead, success in 2025 and beyond hinges on effectively integrating predictive, generative and autonomous agent capabilities. The XOps approach, rooted in structured operational excellence and proactive governance, positions businesses for sustained leadership. Organizations must move beyond isolated AI initiatives toward scalable, governed ecosystems that continuously evolve, shaping their industries and setting new standards for operational excellence and innovation. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
