Creating and managing production-grade agentic AI applications goes beyond just using advanced models. AI teams often face challenges with outdated tools and complex infrastructures, which take time away from innovation. DataRobot’s acquisition of Agnotiq and the Covalent platform aims to simplify this process by integrating AI decision-making, governance, and lifecycle management. This allows teams to concentrate more on building applications instead of dealing with infrastructure issues. With features like customizable AI apps and the ability to leverage various computing resources, AI teams can now develop and deploy agentic AI applications more efficiently, meeting real-world business needs faster and more seamlessly.
Building and operating production-grade agentic AI applications are becoming increasingly essential in today’s tech landscape. However, the journey from developing a prototype to launching a fully operational application can be fraught with challenges. AI teams often find themselves bogged down by complex workflows and rigid infrastructures that hinder innovation.
To navigate this complexity, DataRobot has acquired Agnotiq and its open-source platform, Covalent. This strategic move aims to simplify the entire AI lifecycle, from development to deployment. With Covalent, teams can focus more on the application logic rather than the underlying infrastructure, reducing the time and effort required to bring AI applications to Market.
One of the standout features of DataRobot, enhanced by Agnotiq, is the ability to create AI-driven workflows that are specific to business processes. This means that AI practitioners can easily translate their business needs into actionable workflows. Additionally, the platform offers a comprehensive suite of AI tools and models, allowing for seamless experimentation and deployment.
Covalent enhances the stack by ensuring that agents operate where the data and applications reside. This flexibility allows users to leverage various computing options, including on-premises and cloud solutions, to optimize performance based on cost and speed requirements. By acting as an “orchestrator of orchestrators,” Covalent integrates easily with popular frameworks like Kubernetes, ensuring that AI workloads are executed efficiently across diverse environments.
Despite these advancements, many AI teams still struggle with bringing their applications to production. Two main hurdles they face are building the application and deploying it at scale. Developing an agentic AI application requires a deep understanding of the business context, extensive experimentation with various AI models, and managing infrastructure constraints. Furthermore, scaling these applications involves careful monitoring of performance, governance, and compliance—all of which can be resource-intensive.
Existing AI solutions often fall short, either because they require teams to build complex stacks from scratch or because they lead teams into rigid ecosystems that lack flexibility. DataRobot’s unified approach aims to alleviate these challenges, allowing teams to deploy AI applications more efficiently.
Key benefits of this approach include:
– Customizable AI applications that cater to specific business needs.
– A broad range of AI tools for rapid iteration and deployment.
– Built-in monitoring features to ensure compliance and governance.
By unifying agentic AI capabilities with improved orchestration, DataRobot is setting the stage for more efficient and effective AI application development. This allows teams to innovate without being hindered by the complexities of managing their infrastructure.
In conclusion, the integration of DataRobot and Agnotiq’s Covalent platform marks a significant advancement in agentic AI development. With the new capabilities at their disposal, AI teams can look forward to faster deployment, lower complexity, and the ability to focus on what matters most: delivering innovative AI solutions.
About the Authors:
Dr. Romi Datta is the Vice President of Product for AI Platform at DataRobot, responsible for overseeing capabilities related to AI governance and lifecycle management.
Debadeepta Dey, a Distinguished Researcher, leads strategic research initiatives aimed at advancing deep learning and solving customer challenges.
Nivetha Purusothaman serves as a Distinguished Engineer, focusing on engineering and product initiatives for strategic partnerships.
William Cunningham is a Principal Engineer with expertise in serverless computing and high-performance computing infrastructure.
Tags: AI, DataRobot, agentic AI, Agnotiq, Covalent, AI lifecycle management, production AI applications
What is Agentic AI?
Agentic AI refers to a type of artificial intelligence that can act independently and make decisions on its own. It is designed to handle tasks in the real world, aiming to improve efficiency and solve complex problems for businesses and organizations.
How can Agentic AI benefit my business?
Agentic AI can streamline processes, reduce costs, and enhance decision-making. It can help automate repetitive tasks, analyze large amounts of data quickly, and provide insights that lead to smarter strategies for growth and innovation.
Is Agentic AI ready for enterprise use?
Yes, Agentic AI solutions are developed with businesses in mind. Many are specifically designed for enterprise needs, ensuring they can handle the scale and complexity required by large organizations while integrating smoothly into existing systems.
What types of industries can use Agentic AI?
Agentic AI can be applied across various industries, including finance, healthcare, retail, and manufacturing. Each sector can benefit from its ability to analyze data, automate processes, and improve customer experiences.
How does Agentic AI ensure security and privacy?
Agentic AI platforms prioritize security by implementing strong data protection measures. They comply with regulations and adopt best practices to keep sensitive information safe, ensuring that businesses can trust their AI solutions.
