As the excitement surrounding artificial intelligence (AI) grows, AI agents represent its latest evolution, enabling autonomous decision-making without constant human oversight. Unlike traditional AI, which merely provides recommendations, AI agents can manage entire workflows, such as processing insurance claims or managing inventory. This capability is leading to rapid adoption; a KPMG survey reveals that 88% of organizations are exploring AI agent initiatives, with Gartner predicting that by 2028, over a third of enterprise software will integrate agentic AI.
However, the complexity and opacity of AI agents pose challenges for organizations, including compliance violations, operational failures, and erosion of trust among stakeholders. Their use of large language models (LLMs) allows them to execute tasks independently, but this lack of transparency can hinder understanding and control. To address these challenges, organizations are increasingly investing in AI agent observability, ensuring they can monitor agent performance and behavior for better decision-making and risk management.
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