Agentic AI: Open-Source Models Have the Potential to Outperform Proprietary Systems

Walid Issa, Senior Manager, Solutions Engineering – Middle East & Africa at NetApp, highlights agentic AI’s unique ability to solve complex, multi-step problems requiring reasoning, contextual understanding, and adaptive decision-making, such as predictive analytics and anomaly detection

How does NetApp define Agentic AI, and how does it fundamentally differ from traditional chatbots or rule-based automation?
Agentic AI likely refers to AI systems that exhibit a higher degree of autonomy and agency in decision-making compared to traditional chatbots or rule-based automation. Agentic AI is designed to mimic human-like reasoning and decision-making processes, allowing it to adapt to new situations and make more complex decisions beyond predefined rules. This demands a dynamic, scalable and intelligent data infrastructure capable of delivering real-time access, secure data movement and context aware storage across hybrid environments.

Beyond task automation, what complex, multi-step problems are agentic AIs uniquely positioned to solve that current AI models cannot?
Agentic AI is uniquely positioned to solve complex, multi-step problems that require reasoning, contextual understanding, and adaptive decision-making. These AI systems can handle tasks that involve unstructured data analysis, real-time data processing, and dynamic decision-making based on evolving data patterns, making them suitable for applications such as predictive analytics, anomaly detection, and personalized recommendations that go beyond simple task automation. These use cases involve adapting to real-time data, relying heavily on a secure, performant and context-aware intelligent data infrastructure to function reliably.

How much autonomy should Agentic AI have in decision-making, and where should humans remain in the loop?
The autonomy of Agentic AI in decision-making should be carefully balanced based on the specific use case and potential risks involved. Humans should remain in the loop for critical decisions, ethical considerations, and oversight to ensure transparency, accountability, and regulatory compliance in AI-driven processes. Collaboration between AI systems and human experts can enhance decision-making outcomes and mitigate potential biases or errors. This ideal collaborative balanced model should be supported by a trusted, secure and traceable intelligent data infrastructure.

What infrastructure challenges arise when deploying Agentic AI at scale?
Deploying Agentic AI at scale poses infrastructure challenges related to power consumption, cooling requirements for high-performance computing systems, data processing latency, and network bandwidth for real-time data communication. Ensuring robust infrastructure capabilities, such as optimized storage solutions, efficient data pipelines, and scalable computing resources, is essential for deploying Agentic AI systems effectively and efficiently. This demands an intelligent, scalable and resilient infrastructure with unified storage and data protection technologies.

What’s the next frontier for Agentic AI—will we see AI “agents” collaborating like human teams?
The next frontier for Agentic AI may involve AI agents collaborating like human teams to solve complex problems, share knowledge, and coordinate actions in real-time. Collaborative AI systems could enhance decision-making processes, optimize resource allocation, and enable intelligent automation of tasks that require diverse expertise and adaptive responses, leading to more efficient and effective AI applications across various industries. NetApp’s intelligent data infrastructure with it’s true hybrid multi cloud capabilities, low latency access, and unified control is important to powering this collaborative AI model.

Will open-source models keep pace with proprietary Agentic AI systems, or will there be a widening gap?
Open-source AI models have shown significant progress and adoption in the AI community, with contributions from researchers, developers, and organizations worldwide. While proprietary Agentic AI systems may offer specialized capabilities and competitive advantages, open-source models have the potential to keep pace or even outperform proprietary systems in certain domains. Continued collaboration, knowledge sharing, and advancements in open-source AI technologies could help bridge the gap between proprietary and open-source Agentic AI systems over time. NetApp can provide the performance, governance and hybrid multicloud interoperability needed to bridge the gap to a productive agent ecosystem.