Enterprises Shift from RAG to Complex Agentic AI
Enterprises are beginning to move beyond simple Retrieval-Augmented Generation (RAG) systems toward more complex agentic AI architectures. According to Kore.ai CEO Raj Koneru, agentic AI is increasingly viewed as a foundational layer for automating knowledge work across business functions. This shift requires robust integration with legacy enterprise systems and a continued emphasis on human-in-the-loop oversight for high-stakes processes.
- Agentic AI moves beyond the "retrieve-and-generate" function of RAG by introducing the ability to plan, reason, and autonomously execute multi-step tasks across different enterprise systems like ERP and CRM. This allows agents to not just answer questions, but to complete complex workflows such as processing a refund or managing a procurement cycle. - A key architectural difference is that agentic systems can perform iterative and adaptive retrieval, refining queries and invoking tools based on intermediate results, whereas traditional RAG performs a single retrieval step. This "Agentic RAG" approach allows for more dynamic problem-solving. - Enterprise adoption is accelerating rapidly; one survey found that the number of enterprises using or piloting agentic AI increased sevenfold in just three months. Another 2026 survey reported that 65% of enterprises are already using AI agents, with 100% planning to expand their usage. - Major challenges to implementing agentic AI include integration with legacy systems lacking modern APIs, ensuring data quality and accessibility across silos, and managing new security risks like "identity explosion" from numerous non-human service accounts. - Governance becomes more complex with agentic systems, requiring robust frameworks for responsible AI, bias detection, and explainability to audit AI-driven decisions and comply with regulations like the EU AI Act. - Platforms like Kore.ai are positioning themselves as enterprise-grade "operating systems" for AI agents, providing multi-agent orchestration, governance tools, and pre-built connectors to over 100 enterprise applications. - The market for agentic AI is projected to reach $103.6 billion by 2032, with a significant portion of enterprise software applications expected to include agentic capabilities by 2028. - Early enterprise use cases are focused on IT operations and cybersecurity, where agents can monitor infrastructure health and provide continuous threat detection, as well as in customer service and procurement.
