DuploCloud launches AI DevOps Help Desk to boost automation
The new platform allows DevOps engineers and IT administrators to shift their roles from writing automation scripts to creating AI agents designed to tackle a wide range of end-user needs.
DuploCloud states that as artificial intelligence continues to drive rapid development and scalability within technology teams, the function of DevOps becomes critical yet increasingly challenging. Many current automation tools, the company notes, still rely heavily upon subject matter expertise, and the process of hiring skilled DevOps professionals is both difficult and costly.
According to the company, its platform has already provided support to a large number of high-growth businesses, including start-ups and enterprises, through an automation platform that covers the breadth of DevOps. This includes infrastructure-as-code (IaC), Kubernetes management, cloud services, observability, security, and compliance.
DuploCloud highlights that frequent collaboration with clients and management of thousands of environments naturally led to integrating AI into its operations. The resultant Agentic Help Desk, now part of DuploCloud's core platform, is aimed at enabling customers to scale more quickly, automate more processes, and free up time for other priorities.
Venkat Thiruvengadam, Founder and Chief Executive Officer of DuploCloud, commented on the complexity of operating cloud infrastructure and the slow pace of automation adaptation. "Building and operating cloud infrastructure continues to grow in complexity. The pace of DevOps automation constantly lags behind the ever-changing engineering and security needs of cloud infrastructure. Meaningful developer self-service remains elusive," he said. "DuploCloud's AI DevOps Help Desk represents a strategic leap forward in how DevOps is executed. Achieving unprecedented speed, efficiency, and reliability, fundamentally reshaping cloud operations."
The traditional IT Help Desk is generally structured as a manual, asynchronous model, limited by human resources. DuploCloud's Agentic DevOps Help Desk aims to replace this with a real-time, agent-driven system where user requests are routed directly to the relevant AI agent.
Through the system, users are able to state their requirements in plain language. The designated agent then responds with appropriate context, executes actions within secure permissions, and has the capacity to escalate tasks or collaborate with other agents if needed. The process also integrates human-in-the-loop elements, such as approval workflows, audit trails, screen share capabilities, and real-time user input, all embedded to provide user control without impacting efficiency.
The platform is reported to include an Automation Studio, centralised around an MCP server, which provides tools compatible with various infrastructure environments including Kubernetes, public cloud providers, open telemetry, and continuous integration/continuous delivery (CICD) systems.
Feedback from early adopters indicates that the AI DevOps Help Desk is being used to update infrastructure practices via containerisation and Kubernetes, address performance concerns, and execute cost optimisation procedures. Teams have reported the ability to automate up to 80% of routine DevSecOps tasks using custom agents calibrated to their specific workflows. The automation platform has reduced the time required for new application onboarding from weeks to minutes, and has halved the time to achieve and maintain industry compliance standards such as SOC2, HIPAA, and PCI.
The Agentic Help Desk is currently accessible to customers through an early access programme.
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Newsroom
2 hours ago
- Newsroom
The AI doctor will see you … soon
Comment: Artificial intelligence is already widely used in healthcare. There are now more than 1000 Federal Drug Administration-authorised AI systems in use in the US, and regulators around the world have allowed a variety of AI systems to support doctors and healthcare organisations. AI is being used to support radiologists examining X-rays and MRI scans by highlighting abnormal features, and to help predict how likely someone is to develop a disease based on their genetics and lifestyle. It is also integrated with consumer technology that many people use to manage their health. If you own an Apple watch, it can use AI to warn you if you develop an abnormal heart rhythm. More recently, doctors (including many GPs in Aotearoa New Zealand) have adopted AI to help them to write their medical notes. An AI system listens into the GP-patient conversation and then uses a large language model such as ChatGPT to turn the transcript of the audio into a summary of the consultation. This saves the doctor time and can help them pay closer attention to what their patient is saying rather than concentrating on writing notes. But there are still lots of things we don't know about the future of AI in health. I was recently invited to speak at the Artificial Intelligence in Medicine and Imaging conference at Stanford University, and clinicians in the audience asked questions that are quite difficult to answer. For example, if an AI system used by a doctor makes a mistake (ChatGPT is well known for 'hallucinating' incorrect information), who is liable if the error leads to a poor outcome for the patient? It can also be difficult to accurately assess the performance of AI systems. Often studies only assess AI systems in the lab, as it were, rather than in real world use on the wards. I'm the editor-in-chief of a new British Medical Journal publication, BMJ Digital Health & AI, which aims to publish high-quality studies to help doctors and healthcare organisations determine which types of AI and digital health technologies are going to be useful in healthcare. We've recently published a paper about a new AI system for identifying which artery is blocked in a heart attack, and another on how GPs in the UK are using AI for transcribing their notes. One of the most interesting topics in AI research is whether generative AI is better than a doctor for general purpose diagnosis. There seems to be some evidence emerging that AI may be starting to be better than doctors at diagnosing patients when given descriptions of complex cases. The surprising thing about this research is that it found that an AI alone might be more accurate than when a doctor uses an AI to help them. This may be because some doctors don't know how to use AI systems effectively, indicating that medical schools and training colleges should incorporate AI training into medical education programmes. Another interesting development is the use of AI avatars (simulated humans) for patient pre-consultations and triage, something that seems likely to be implemented within the next few years. The experience will be very similar to talking with a human doctor and the AI avatar could then explain to the real doctor what that they found and what they would recommend as treatment. Though this may save time, a balance will need to be struck between efficiency and patients' preferences – would you prefer to see an AI doctor now or wait longer to see a human doctor? The advancement of AI in healthcare is very exciting but there are risks. Often new technology is implemented without considering so-called human factors. These can have a big impact on whether mistakes are made using the new system, or even whether the system will get used at all. Clinicians and patients quickly stop using systems that are hard to use or that don't fit into their normal work routines. The best way to prevent this is to use 'human-centred design', where real people – doctors and patients – are included in the design process. There is also a risk that unregulated AI systems are used to diagnose patients or make treatment decisions. Most AI systems are highly regulated – patients can be reassured that any AI involved in their care is being used safely. But there is a risk that governments may not keep up with the accelerating development of AI systems. Rapid, large-scale adoption of inaccurate healthcare-related AI systems could cause a lot of problems, so it is very important governments invest in high-quality AI research and robust regulatory processes to ensure patient safety. Chris Paton will be giving a public lecture about AI in healthcare at the Liggins Institute on August 14 at 6pm. Register here.
Techday NZ
14 hours ago
- Techday NZ
EY & ACCA urge trustworthy AI with robust assessment frameworks
EY and the Association of Chartered Certified Accountants (ACCA) have released a joint policy paper offering practical guidance aimed at strengthening confidence in artificial intelligence (AI) systems through effective assessments. The report, titled "AI Assessments: Enhancing Confidence in AI", examines the expanding field of AI assessments and their role in helping organisations ensure their AI technologies are well governed, compliant, and reliable. The paper is positioned as a resource for business leaders and policymakers amid rapid AI adoption across global industries. Boosting trust in AI According to the paper, comprehensive AI assessments address a pressing challenge for organisations: boosting trust in AI deployments. The report outlines how governance, conformity, and performance assessments can help businesses ensure their AI systems perform as intended, meet legal and ethical standards, and align with organisational objectives. The guidance comes as recent research highlights an ongoing trust gap in AI. The EY Response AI Pulse survey found that 58% of consumers are concerned that companies are not holding themselves accountable for potential negative uses of the technology. This concern has underscored the need for greater transparency and assurance around AI applications. "Rigourous assessments are an important tool to help build confidence in the technology, and confidence is the key to unlocking AI's full potential as a driver of growth and prosperity." Marie-Laure Delarue, EY's Global Vice-Chair, Assurance, expressed the significance of the current moment for AI: "AI has been advancing faster than many of us could have imagined, and it now faces an inflection point, presenting incredible opportunities as well as complexities and risks. It is hard to overstate the importance of ensuring safe and effective adoption of AI. Rigourous assessments are an important tool to help build confidence in the technology, and confidence is the key to unlocking AI's full potential as a driver of growth and prosperity." She continued, "As businesses navigate the complexities of AI deployment, they are asking fundamental questions about the meaning and impact of their AI initiatives. This reflects a growing demand for trust services that align with EY's existing capabilities in assessments, readiness evaluations, and compliance." Types of assessments The report categorises AI assessments into three main areas: governance assessments, which evaluate the internal governance structures around AI; conformity assessments, determining compliance with laws, regulations and standards; and performance assessments, which measure AI systems against specific quality and performance metrics. The paper provides recommendations for businesses and policymakers alike. It calls for business leaders to consider both mandatory and voluntary AI assessments as part of their corporate governance and risk management frameworks. For policymakers, it advocates for clear definitions of assessment purposes, methodologies, and criteria, as well as support for internationally compatible assessment standards and market capacity-building. Public interest and skills gap Helen Brand, Chief Executive of ACCA, commented on the wider societal significance of trustworthy AI systems. "As AI scales across the economy, the ability to trust the technology is vital for the public interest. This is an area where we need to bridge skills gaps and build trust in the AI ecosystem as part of driving sustainable business. We look forward to collaborating with policymakers and others in this fascinating and important area." The ACCA and EY guidance addresses several challenges related to the current robustness and reliability of AI assessments. It notes that well-specified objectives, clear assessment criteria, and professional, objective assessment providers are essential to meaningful scrutiny of AI systems. Policy landscape The publication coincides with ongoing changes in the policy environment on AI evaluation. The report references recent developments such as the AI Action Plan released by the Trump administration, which highlighted the importance of rigorous evaluations for defining and measuring AI reliability and performance, particularly in regulated sectors. As AI technologies continue to proliferate across industries, the report argues that meaningful and standardised assessments could support the broader goal of safe and responsible AI adoption both in the private and public sectors. In outlining a potential way forward, the authors suggest both businesses and governments have roles to play in developing robust assessment frameworks that secure public confidence and deliver on the promise of emerging technologies.
Techday NZ
14 hours ago
- Techday NZ
Cloudera acquires Taikun to boost data & AI across all clouds
Cloudera has acquired Taikun, a platform provider for managing Kubernetes and cloud infrastructure, to strengthen its data and AI platform across hybrid and multi-cloud environments. Cloudera's move allows the company to accelerate deployment of its platform, including Data Services and artificial intelligence workloads, across a range of settings from public clouds and on-premises data centres to sovereign and air-gapped environments, all managed through a unified control structure. The need for more reliable and scalable data infrastructure has grown as enterprise IT environments become increasingly complex and distributed. The acquisition of Taikun provides Cloudera with native Kubernetes capabilities aimed at enabling simpler and more flexible operations, even within highly regulated or distributed contexts. Cloud-like operation anywhere Taikun's technology will give Cloudera an integrated compute layer, uniting deployment and operations throughout the information technology stack. This aims to deliver a cloud-like experience consistently across different environments. Customers will gain the flexibility to run data and AI workloads in data centres, clouds, or hybrid settings without compromising on performance or control. Among the benefits highlighted are support for highly regulated environments such as GovCloud, Sovereign Cloud, and air-gapped data centres. Taikun's integration is expected to increase operational efficiency through zero-downtime upgrades and improved resource optimisation. Customers will also have the option to integrate a variety of tools and databases from Cloudera's ecosystem and third-party providers using a "bring your own engine" approach. Cloudera described the acquisition as part of its approach to ensuring long-term flexibility for customers, enabling adaptation to future business mandates and maintaining alignment with changing requirements for data and AI workloads. Executive perspectives "This acquisition marks a pivotal step in our mission to bring the cloud experience wherever enterprise data resides," said Charles Sansbury, CEO, Cloudera. "By integrating Taikun's container-native platform in our stack, we are removing operational barriers and enabling our customers to unlock faster insights, make smarter decisions, and drive real-time action in every corner of their business." Following the deal, Taikun's engineering team will join Cloudera's broader engineering, product, and support organisation, with Taikun's base in the Czech Republic becoming a European development hub for Cloudera. "Our acquisition by Cloudera marks a pivotal moment for us," said Adam Skotnicky, former CEO of Taikun, "Our advanced cloud-native computing platform will enable customers from across the globe to deliver and deploy services and applications seamlessly, whether that's in the data centre or in multi-cloud environments. Only Cloudera is the right organisation for us to join during this critical moment for data and AI." Industry analysts noted the challenges organisations face due to dispersed data and application management, and suggested that the merger could provide improvements for AI and analytics across diverse environments. "Organisations are suffering more than ever from fragmented data and application management across diverse infrastructures, increasing complexity, costs, and limiting data/AI initiatives," said Sanjeev Mohan, Principal, SanjMo. "The result: compromise on workload placement and data analysis. With Cloudera's acquisition of Taikun and integration in Cloudera's platform, organisations can now run AI and analytics anywhere their data lives - from cloud to edge - accelerating insights, empowering smarter choices, and driving real-time responses throughout their organisation." Acquisition strategy This is Cloudera's third acquisition in 14 months, following the purchase of Verta's operational AI platform in May 2024 and Octopai's data lineage and catalogue solution in November 2024. These acquisitions have been aimed at reinforcing Cloudera's platform and ensuring customers have flexibility in how and where they manage data and AI workloads. Follow us on: Share on:
