... Access Research Already a Gartner client? The AI code assistant market continues to burn hot as rapid innovation and intense competition is fueled by disruptive advances and new entrants. Use this research to compare vendors, navigate key trends, and select the right AI code assistant to enhance devel ...
Analyst(s): Haritha Khandabattu | Philip Walsh | Arun Batchu | Matt Brasier | Keith Holloway
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... Gartner for Data & Analytics Leaders Accelerate outcomes with AI-powered tools, on-demand insights and interactive experiences. Top questions from data and analytics leaders — and how we answer them How can I turn data, analytics and AI into true drivers of strategic growth? Adopt an outcomes‑led D&A st ...
... Access Insights Already a Gartner client? Software engineering leaders face a rapidly evolving landscape of platforms and frameworks for building AI-powered solutions. Leaders must use a layered stack that balances enterprise governance with developer flexibility to streamline delivery of AI agents and ...
... with application delivery speed and maintenance are increasingly investing in enterprise low-code application platforms. This note guides software engineering leaders to learn from the implementation experience of their peers shared on Gartner Peer Insights. Overview Peer Lessons Learned Gartner Recommended ...
... rapidly emerging as viable options for different use cases. Example technology: Multimodal generative AI AI engineering enables safe, scalable deployment Scaling GenAI programs demands robust engineering. This requires tools and frameworks for building, governing and customizing GenAI-powered applications. ...
... SWELs must integrate AI/ML models into applications, and teach and upskill enterprise development teams on how this will change their responsibilities and foster cooperation with the data science teams. SWELs should define the process for integrating the ModelOps workflow with their DevOps workflow. ...
... client? The rise of agentic AI requires applications to support both predictable interactions and nondeterministic, goal-oriented systems. Software engineering leaders must integrate agentic AI capabilities into composable architecture and standardize agent integration and operations to drive AI agent adoption ...
... all custom-built AI agents the same regardless of autonomy and scope will be the primary driver of AI agent failure in the enterprise. Software engineering leaders must implement autonomy-level validation gates and classify custom-built AI agents by their highest intended autonomy level. Gartner Insights ...
... AI agents create continuous and opaque cost exposure. Software engineering leaders must embed FinOps into AI agent design and operations to monitor cost, behavior, and value in real time. This research explains how FinOps enables continuous optimization, financial accountability, and scalable autonomy ...
Analyst(s): Bill Blosen | Aaron Harrison | Deacon D.K Wan | Tigran Egiazarov