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... The proliferation of AI technologies without a cohesive integration strategy threatens business value by increasing interoperability challenges and undermining existing governance. Software engineering leaders must guide their teams in building secure, scalable, and governed AI applications using MCP ...
... The rapid rise of enterprise AI assistants allows employees to build AI agents and applications with little IT oversight; this creates extreme enterprise risk. Tech services leaders who address the critical need for agent harness engineering services and agent management platforms will mitigate enterprise ...
... In most cases, organizations fail to include evaluations in the design of their GenAI solutions, making it impossible to measure reliability once AI solutions transition into production. AI architects and data scientists should use this note to successfully design and implement GenAI evaluations. Overview ...
... Already a Gartner client? AI systems introduce risks that traditional observability cannot detect, from hallucinations and bias to compliance violations. This research defines AI observability and gives heads of I&O the insights needed to evaluate, instrument, and govern AI systems in production. Overview ...
... In a market that glorifies companies that deploy “AI agents” and “agentic AI,” the terms are now overused. But what is this technology, really? This research explains what AI agents and agentic AI are, what differentiates them from other technologies, and how they may impact the future of service and ...
... Gartner client? The rapid advancement of artificial intelligence is challenging leaders to coordinate initiatives across the enterprise. CIOs and other digital leaders can use this step-by-step guide to integrate and prioritize the resources needed to advance and scale AI initiatives across the enterprise. ...
Analyst(s): Jessica Harrison | Aviral Vyas | Janelle Hill | Maheen Ahmad | Whit Andrews