AI Strategy for Defensive Fortification in Four Phases
In the rapidly evolving world of agentic AI, the competitive landscape is being redefined. Unlike traditional software businesses, competitive positions in agentic AI must be deliberately architected from the outset to ensure long-term strength.
The journey to dominance in agentic AI involves several phases: foundation, differentiation, dominance, and expansion. A company systematically builds competitive advantages by developing three interconnected layers of moats—data moats, behavioral moats, and workflow moats—which compound over time to create durable barriers to entry.
Data Moats
The first layer, data moats, is built by creating unique, high-quality, and high-signal data assets. These assets train or fine-tune AI agents to deliver value that competitors find difficult to replicate. This often comes from proprietary feedback loops where users actively correct and improve AI outputs, enhancing the AI’s performance continuously.
For example, specialized legal tech startups create unique data from lawyer feedback to sharpen M&A document review AI continuously.
Behavioral Moats
The second layer, behavioral moats, is about designing user interaction patterns that generate reinforcing loops. These loops lock users deeper into a product ecosystem that gets smarter with usage, increasing switching costs and user dependency on the agentic AI tool.
Workflow Moats
The third layer, workflow moats, is achieved by deeply embedding AI agents into customers’ crucial workflows. This integration creates high switching costs as the AI agent orchestrates multiple real-time systems and workflows seamlessly.
These layers are supported by infrastructural innovations like the Model Context Protocol (MCP), which enables agentic AI products to chain tasks, interact with live data and external APIs, and maintain persistent memory and permissions across sessions. MCP is becoming foundational, akin to “USB-C for AI agents,” enabling companies to create ecosystems where AI acts autonomously across systems on users’ behalf.
This strategy involves avoiding the trap of treating AI as a mere feature or a commodity model that competitors can replicate rapidly. Instead, companies focus on creating compounding advantages by owning unique data, reinforcing user behavior that improves the AI, and embedding the AI agent deeply in essential workflows that others cannot easily copy.
In practice, this means starting small with highly specific business pain points, embedding agentic AI tightly into mission-critical workflows to become indispensable, leveraging infrastructure like MCP and unified data platforms to coordinate AI agents effectively, and rapidly iterating with an "agentic lens" to automate high-volume, repetitive tasks with specialized AI agents.
This three-layer moating strategy creates a sustainable competitive advantage by continuously compounding the value and defensibility of the AI product, rather than relying on transient advantages from the underlying AI models themselves.
The competitive moats in agentic AI are a focus of ongoing research and development. As the competitive landscape for agentic AI continues to evolve, it is clear that the concept of competitive moats is a significant aspect of the agentic AI framework. Patience, strategic focus, and resisting shortcuts are essential for building long-term strength in agentic AI.
In the realm of agentic AI, businesses prioritize the strategic creation of moats, comprised of data moats, behavioral moats, and workflow moats, to ensure long-term strength. For instance, legal tech startups might employ user feedback to build unique data moats, enhancing their AI's M&A document review capabilities. Furthermore, these moats, particularly data moats, are accentuated by infrastructural innovations like the Model Context Protocol (MCP), enablers of AI autonomy across various systems.
