The story of technological innovation often unfolds in three interdependent layers: infrastructure, software, and applications. Together, these layers form the foundation for transformative business models and paradigm shifts in how we work, connect, and create value.
Take the Internet revolution as an example. In the 1990s, we witnessed the infrastructure phase—fiber-optic cables, semiconductors, ISPs, and networking hardware provided the physical and digital backbone. Then came the rise of software platforms, such as Internet Explorer and Netscape, which opened the door for mass adoption. Finally, in the 2000s, the explosion of applications like Amazon, Google, and Facebook redefined how we shop, search, and socialize, building on both the infrastructure and software layers to deliver unparalleled utility.
Today, artificial intelligence (AI) is following a similar trajectory. We are still in the infrastructure phase, where companies like AMD, Broadcom, and Nvidia are building the foundational hardware and computing systems necessary for AI to thrive. Software platforms such as OpenAI’s APIs and frameworks like TensorFlow are enabling developers to experiment and create. On top of these two layers, the third wave—the application phase—is beginning to take shape. This is where agentic AI emerges, redefining productivity, decision-making, and business operations. The most significant disruptions will come from those who can harness all three layers effectively.
What Is Agentic AI?
At its core, agentic AI represents a significant evolution in the application layer. It refers to artificial intelligence systems capable of making autonomous decisions. Unlike reactive AI, which responds to specific inputs or predefined rules, agentic AI operates proactively, autonomously assessing situations, weighing options, and executing actions.
To better understand this distinction, consider the progression through the three layers:
- Infrastructure: The self-driving car is only possible because of advancements in AI chips (e.g., Nvidia’s GPUs), LiDAR technology, and 5G networks.
- Software: Machine learning frameworks enable training models for perception, navigation, and decision-making.
- Application: The car as a whole becomes an agent, interpreting real-time data and making decisions such as braking, accelerating, or turning—all without human intervention.
In the business world, this progression mirrors how companies are moving from reactive tools that assist decision-making to autonomous collaborators that execute processes, learn from outcomes, and improve systems on their own.
The Layered Evolution of Agentic AI Applications
Much like the internet era, where the combination of infrastructure, software, and application layers unlocked exponential value, the AI era will see innovations layered in a similar way. Below, we explore how agentic AI is driving this transformation across industries.
1. Marketing: Smarter Campaigns from Infrastructure to Application
- Infrastructure: Advanced GPUs power the vast datasets required for marketing insights, while cloud providers like AWS and Azure offer scalable compute resources for AI.
- Software: Tools like OpenAI’s APIs, Salesforce Einstein, and HubSpot integrate AI into marketing workflows. These platforms act as the middleware between infrastructure and the end-user.
- Applications: Platforms like Drift and Persado take the next step by creating autonomous agents that close sales, optimize ad spend, and generate creative copy. For example:
- Drift’s conversational AI agents actively prioritize leads and even close small deals without human intervention.
- Persado autonomously generates and refines ad copy, removing guesswork from creative decision-making.
2. Cybersecurity: Autonomous Threat Detection
- Infrastructure: AI-native infrastructure—such as CrowdStrike’s endpoint protection architecture or Cloudflare’s global network—is critical for handling the massive flow of real-time threat data.
- Software: AI-powered platforms process and analyze this data, such as CrowdStrike’s Falcon or Cloudflare’s intelligent edge technology.
- Applications: Agentic AI systems autonomously detect and neutralize threats. For instance:
- CrowdStrike eliminates malware before it infiltrates endpoints.
- Cloudflare blocks DDoS attacks in real-time while optimizing network performance autonomously.
3. Ad Tech: Real-Time Decisions at Scale
- Infrastructure: The Trade Desk relies on high-performance compute infrastructure to process petabytes of data across global advertising platforms.
- Software: Machine learning algorithms and AI frameworks enable the analysis of vast datasets in milliseconds.
- Applications: Agentic AI automates ad buying, optimizes ad placements, and reallocates budgets dynamically for maximum ROI. This eliminates manual guesswork, letting advertisers focus on high-level strategy.
4. Business Technology: From Automation to Autonomy
- Infrastructure: Databricks and UiPath depend on scalable cloud and AI hardware to support the massive data flows and compute needs of enterprise clients.
- Software: Integrated platforms like ServiceNow enable AI-enhanced workflow management at scale.
- Applications: Autonomous agents handle increasingly complex workflows:
- UiPath’s bots analyze processes, detect inefficiencies, and implement improvements without human intervention.
- Databricks automates the end-to-end process of data ingestion, cleaning, modeling, and deployment, allowing businesses to act on insights instantly.
The Future of Work: Scaling Decisions with Humans and AI
Imagine a business team of 10 human employees working with 100 AI agents. These agents specialize in tasks like analyzing market trends, managing ad campaigns, optimizing supply chains, and even designing marketing content. They operate 24/7, making autonomous decisions, while the human team focuses on strategy, creativity, and oversight. This isn’t science fiction—it’s the future of business.
To thrive in this new era, businesses need to start preparing today.
Here’s how:
- Embrace Collaboration Between Humans and AI: Train your workforce to collaborate with AI agents, emphasizing skills like creativity, leadership, and ethical decision-making—areas where humans excel.
- Experiment and Iterate: Agentic AI is still in its early stages, and the most successful companies will be those willing to experiment with new tools, processes, and business models.
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