What makes agentic AI autonomous or able to take actions independently is its ability to interpret data, predict outcomes, and make decisions, learning from new data — unlike traditional RPA, which falters when encountering unexpected data, said Cameron Marsh, senior analyst at Nucleus research.
This adaptive nature of agentic AI, according to Chada, can help enterprises increase efficiency by handling complex, variable tasks that traditional RPA can’t manage, such as the roles of a claims adjuster, a loan officer, or a case worker, provided that it has access to the necessary data, workflows, and tools required to complete the task.
Software vendors are already touting agentic AI offerings with access to those resources, including the likes of Salesforce’s Agentforce, Microsoft’s Copilot-based autonomous Agents, ServiceNow’s AI Agents, Google’s Vertex AI Agent Builder, Amazon Bedrock Agents, and IBM’s watsonx Agent Builder, with more are likely to follow.
