Agentic AI: Evolution, Challenges, and Solutions

Agentic AI represents the next major evolution in artificial intelligence tools, relying on autonomous agents equipped with advanced capabilities to enhance business and organizational efficiency. These agents can make decisions, execute actions, and even self-learn to achieve specific goals. Gartner predicts that one-third of enterprise software applications will incorporate Agentic AI by 2028, up from virtually no presence in 2024. This development will enable companies to empower their teams to focus on high-impact strategic decisions, provide faster customer responses, and drive innovation and sustainable growth.

Challenges of Adopting Agentic AI

Despite its immense potential, the path to adopting Agentic AI faces significant challenges for organizations that have not adapted their data infrastructure. Gartner predicts that over 40% of Agentic AI projects will be abandoned by the end of 2027, citing media hype, exorbitant costs, and technical complexity as primary reasons for this failure. The true power lies in the ability of multiple agents to communicate and coordinate effectively across a connected, reliable, and real-time data infrastructure. However, Large Language Models (LLMs), which form the core of these agents' operations, remain susceptible to hallucinations, meaning the generation or misapplication of inaccurate information. No organization can ignore these risks, as the consequences of errors could be severe, leading to personal harm or significant legal liabilities in critical sectors such as healthcare and insurance.

Data Requirements for Successful Agentic AI

Deploying multi-agent systems in a production environment is complex. Agentic AI projects often show impressive results as prototypes but face significant difficulties when scaling to become realistic and operational systems. Agentic AI requires continuous access to real-time data from on-premise and cloud enterprise databases, as well as streaming data, external data sources, and historical data. Achieving this integration while minimizing or completely eliminating the likelihood of AI errors represents a major technical challenge. This is where contextual data plays a crucial role in bridging knowledge gaps, as Large Language Model (LLM) data may not be fully up-to-date and could even be significantly outdated.

The Importance of Integration and Data Governance in the Age of Agents

This challenge highlights a broader shift in the technological landscape: companies have begun integrating advanced analytics with operational systems to support Agentic AI. This integration aims to transition from information overload to extracting clear, actionable insights. However, most organizations still struggle to deliver the right data to the right people at the right time, which explains why many experimental Agentic AI projects fail.

To maximize the benefits of Agentic AI, organizations must integrate data from diverse sources in a way that ensures user trust. Strict security controls, clear permissions, and comprehensive audit logs will be crucial factors in ensuring data security, accuracy, and responsible use. Organizations must cleanse and unify their data and implement stringent data governance. This ensures that when Large Language Models and contextual data are used, they can generate the precise intelligence needed to effectively automate tasks. Data must remain current, available in real-time, and consistently reliable.

Agent Coordination and Supporting Technical Standards

AI agents do not operate in isolation; rather, they require context sharing, action coordination, real-time decision-making, and seamless integration with external tools, APIs, and enterprise-level data sources. Modern open standards in Agentic AI, such as the Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication, appear highly promising for enabling AI agents to communicate effectively, access information, execute tasks, and make decisions across complex organizational workflows. Persistent memory capabilities are essential for effective prompt engineering, which enhances AI understanding, maintains context, and increases the ability to generate relevant and personalized responses. Without memory extending beyond the current query, it would be very challenging to develop production-ready Agentic AI systems.

Modern Data Architecture to Empower Agentic AI

Traditional data platforms, such as Data Warehouses and Data Lakes, which primarily served SQL analysts and data engineers, are no longer sufficient to meet the demands of the modern era. The current technical landscape requires flexible data access for a wide range of use cases, including machine learning, business analytics, reporting, dashboards, and next-generation Agentic AI applications. The biggest challenge in building a data infrastructure dedicated to Agentic AI lies in its ability to operate efficiently, scale effectively, and be cost-effective. At the core of this infrastructure are the principles of data governance, strict access control, continuous observability, and comprehensive cybersecurity.

Is Data Fabric the Optimal Data Strategy for the Age of Agentic AI?

The success of any dedicated data layer for AI heavily relies on easy access to high-quality data. This is where Data Fabric emerges, acting as an intelligent data layer that connects and manages data from all enterprise systems in real-time. This architecture helps eliminate data fragmentation by seamlessly integrating every source, ensuring data consistency and continuous accessibility. Data Fabrics utilize advanced tools such as Metadata Management, Knowledge Graphs, and Semantic Layers to add context and meaning to data. This enables AI agents to deeply understand complex business contexts and relationships between different data points. A Data Fabric meets these fundamental needs of Agentic AI by providing the benefit of unified data to feed AI models that deliver accurate, context-aware insights to support intelligent decision-making and efficient task automation.

Data Fabric adopts a centralized data architecture and a unified governance model, which facilitates efficient data sharing and integration across the entire organization. In contrast, Data Mesh represents a decentralized approach to data management. In Data Mesh, data ownership remains within its own domain, and each domain is responsible for defining, delivering, and governing its own data products. This approach relies heavily on the interaction between people and processes. Agentic AI systems require multiple agents that are unified in nature to ensure effective coordination. However, having a unified data infrastructure and governance adds another layer of complexity, requiring additional coordination between individuals and processes. Some initial successes in implementing Agentic AI systems within organizations can be linked to centralized data infrastructures based on Data Fabrics, indicating their effectiveness in this context.

It is essential that AI agents operate within strict secure and ethical boundaries, that their actions align with specified use cases, internal organizational policies, and that they fully comply with applicable regulations and laws. Data Lineage, comprehensive and enhanced monitoring, the integration of Ethical AI Frameworks, and robust enterprise-level cybersecurity are all critically important for any production-ready and trustworthy Agentic AI systems.