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5 Steps to Getting Started with Agentic AI

From Static Models to Autonomous Agents

As artificial intelligence continues to evolve organizations are entering a new era; one defined not by static models waiting for instructions, but by intelligent agents that think, act, and learn independently. This transition marks a significant shift in enterprise AI strategy. The challenge is no longer just about accelerating training cycles or optimizing inference workloads. It’s about enabling systems that can operate continuously, interpret intent, access distributed data, and adapt their behavior in real time. To achieve this, infrastructure must evolve too, becoming not just faster, but fundamentally smarter.

1. Understand Agentic AI – What It Is and Why It Matters

What It Means to Move From Models to Agents

Welcome to the age of agentic AI. These systems represent a departure from traditional, prompt-based models. They are capable of identifying goals on their own, coordinating complex tasks, retrieving contextual information across environments, and orchestrating downstream processes—without human intervention. Examples include customer service bots that continuously improve with user feedback, automated research assistants that compile contextually relevant information, and industrial systems that optimize operations in real time without human input. According to Gartner®, “by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024.” But to realize that growth, organizations must embrace infrastructure that can support intelligent, autonomous behavior at scale.

2. Identify the Gaps in Traditional Infrastructure

Why Agentic AI Demands Intelligent Infrastructure

Traditional AI systems are reactive by design. They await explicit instructions, execute narrowly defined tasks, and operate within siloed environments. Agentic AI disrupts this model entirely. It brings the ability to proactively set goals, synthesize data across domains, learn from historical context, and trigger actions in real time. These systems behave more like intelligent collaborators than tools, responding dynamically to changing inputs and orchestrating workflows across hybrid environments.

To support this level of autonomy, the infrastructure must deliver more than compute and storage. It must enable persistent memory, fast metadata-aware search, and real-time access to multi-modal data, spanning formats like video, text, and sensor data. It must also provide orchestration across the edge, core, and cloud, ensuring continuity and consistency across distributed workloads. Most legacy architectures, designed for static batch processing, are ill-suited for this kind of dynamic cognition. What’s needed is an agentic AI architecture built not only for performance, but for awareness; one that enables autonomous decision-making, context retention, and real-time responsiveness.

3. Design Intelligent Infrastructure for Agentic AI Workflow

Define what makes infrastructure intelligent

In today’s AI environment, raw speed is no longer the benchmark. Throughput alone does not enable understanding. Intelligent infrastructure stands apart by providing systems with the context to interpret data and act on it intelligently.

This begins with deep metadata indexing that gives AI systems visibility into the nature, relevance, and relationships of the data they’re handling. It includes persistent memory that allows agents to remember and learn from past interactions. Event-driven workflows further allow systems to respond automatically as new data becomes available, triggering analysis, model updates, or downstream actions without manual intervention. When multimodal access is combined with orchestration across diverse environments, AI systems gain full situational awareness.

This shift, from throughput to thought, is the backbone of the DDN Data Intelligence Platform. It’s not just about moving data quickly; it’s about enabling AI to reason, adapt, and deliver outcomes autonomously.

4. Enable Secure, Scalable Agentic AI in Production

Security and Scale for Autonomous Workloads

Deploying agentic AI in production environments requires more than intelligence. It also demands robust security, governance, and scalable control. AI agents must be empowered to act independently, but only within clearly defined parameters. Infrastructure must enable this with precision. End-to-end encryption and granular access controls protect sensitive information while supporting regulatory compliance. These capabilities are powering production-grade agentic AI workflows — from real-time fraud detection, LLM deployment, AI-assisted drug discovery, and autonomous vehicle decision systems across industries including financial services, life sciences, manufacturing, and automotive.

With a modern data platform in place, organizations can unlock higher levels of performance, automation, and control. These improvements will be foundational to enabling agentic systems that learn and evolve over time.

Built on a cloud-native architecture, secure multi-tenancy allows multiple AI workloads to run in parallel without risk of data leakage or cross-contamination. Flexible APIs support scalable deployment and orchestration, enabling organizations to expand their agentic AI footprint confidently, without compromising performance, control, or data sovereignty. The DDN Data Intelligence Platform was designed with this balance in mind.

5. Assess Your Readiness for AI Agent Workflows

Is Your Infrastructure Ready for AI Agent Workflows?

Before adopting agentic AI, organizations must evaluate whether their infrastructure can support a complete agent workflow — from ingesting real-time data to triggering autonomous actions. Use this quick readiness check as a starting point:

Key Question

  • Can your platform ingest and serve multimodal data in real time?
  • Is it metadata-aware?
  • Can it scale seamlessly across cloud, core, and edge?
  • Are workloads isolated and encrypted?

Why It Matters

  • Determines agent responsiveness
  • Enables agents to search, contextualize, and orchestrate effectively
  • Ensures consistent performance and autonomy across environments
  • Guarantees data security, privacy, and regulatory compliance

If the answer to any of these is “no,” DDN offers a clear path forward—with a modular, software-defined platform purpose-built for the demands of autonomous AI.

Real-World Deployments: Agentic AI in Action

Across sectors, leading organizations are already deploying DDN to operationalize agentic AI and deliver breakthrough outcomes:

Financial Services
Retrieval-augmented generation (RAG) workflows are now autonomously monitoring compliance, ingesting regulatory updates, correlating them with live transactions, and flagging anomalies, at machine speed and scale.

Life Sciences
Protein structure analysis pipelines, powered by event-driven triggers, are automatically launching downstream inference and training tasks the moment new research data becomes available, accelerating discovery in genomics and drug development.

Autonomous Vehicles
DDN supports one of the world’s largest AI inference environments, driving real-time decision-making for over 100,000 GPUs analyzing video and sensor data with sub-millisecond latency.

Telecommunications
Edge-deployed AI agents equipped with graph search and metadata indexing proactively identify and address network anomalies before customers are impacted; dramatically improving reliability and service continuity

These are not pilot projects, they are production-grade systems, demonstrating what’s possible when infrastructure is designed to support intelligence, not just execution.

DDN: Powering the Next Era of AI

Agentic AI isn’t a buzzword, it’s the next frontier of digital transformation. It promises systems that learn, reason, and act independently, but only if supported by infrastructure capable of continuous cognition and real-time orchestration.

With DDN, you don’t just get faster AI, you get smarter AI. You get infrastructure that can adapt to evolving workloads, govern data across geographies, and deliver insights when and where they’re needed most.

Last Updated
Jul 17, 2025 4:05 AM
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