Capgemini-商务，会见代理ai-自主数据工程中的代理人工智能（英）

Agentic AI for autonomous data engineeringImagine data ecosystems that think. What if your data platform could manage itself?It isn’t “what if” anymore. It’s “what’s next” with agentic AI. The complexities of modern data landscapes demand a new level of automation and intelligence. Agentic AI is answering that call, bringing forth a paradigm shift where AI agents independently navigate and optimize data ecosystems, delivering unprecedented efficiency and agility. As the landscape of data engineering continues to evolve, the integration of agentic AI is shifting the focus from reactive data engineering to proactive autonomous systems. 2Agentic AI for autonomous data engineeringAgentic AI refers to artificial intelligence systems that can autonomously pursue specific objectives with minimal human guidance. At its core, it comprises AI agents, which are essentially machine learning models designed to emulate human-like decision-making processes to solve problems in real time.Agentic AI often builds upon the foundations of generative AI by utilizing large language models (LLMs) to operate effectively within dynamic environments. While generative models excel at creating new content based on learned patterns, agentic AI extends this capability by applying the outputs generated by these models toward the accomplishment of specific tasks. In a nutshell, generative AI is AI that creates, whereas agentic AI is AI that acts.Several characteristics underpin the functionality and potential of agentic AI that includes autonomy, perception, goal orientation, learning, adaptability, reasoning, decision-making, and execution. These characteristics are enabled in the AI agent with key modular components given below.Demystifying agentic AI3Agentic AI for autonomous data engineeringCognitive moduleAgent's brain for thinking and decision-makingActionmoduleTranslating agent’s decision to execute actionsLearning moduleImprove performance from experience and feedbackMemorymoduleRetain information over time and maintain contextCommunication moduleExchanges information with other agents, humans, or systemsPerception moduleCollect and interpret input dataKnowledge moduleKnowledge base for context-aware responsesMonitoring and evaluation moduleTracks agent's performance and provides metrics for improvementUser/EventKey components of AI agentPerceive, reason, act, and learnAI agent4Agentic AI for autonomous data engineeringConvergence of data engineering and agentic AIData engineering has emerged as a vital discipline focused on the design, construction, and maintenance of systems that enable the effective management and transformation of raw data into a usable and insightful resource.In parallel, the field of artificial intelligence is undergoing a significant evolution with the advent of agentic AI. This paradigm represents a departure from traditional AI models that primarily react to specific inputs or follow predefined rules.The intersection of these two dynamic field