Agentic AI Stack

The agentic AI revolution has created a new technology stack that extends far beyond simple API calls to foundation models. Understanding this stack—and where sustainable competitive advantages can be built within it—is crucial for any company seeking to create defensible positions in the AI market.

Unlike traditional software stacks, where value often concentrates at the application layer, the agentic AI stack presents a more nuanced picture. Some layers offer tremendous moat-building potential, while others are rapidly commoditizing.

Key to success lies in understanding these dynamics and focusing resources on the layers where sustainable advantages can be built.

Layer 1: Foundation Models (Commoditized Base)

What It Is: The large language models that provide core AI capabilities—Claude, GPT-4, Gemini, Llama, and others. These models handle reasoning, language understanding, and generation.

Moat Potential: LOW

Foundation models represent the most commoditized layer of the stack. While building these models requires enormous capital and expertise, accessing them has become trivial through APIs. Companies like OpenAI, Anthropic, and Google compete primarily on performance and price, leading to rapid commoditization.

Strategic Reality: Unless you're building foundation models with hundreds of millions in funding, this layer offers minimal differentiation potential. Model capabilities are becoming table stakes, and switching costs between providers are minimal.

Business Implications: Treat foundation models as infrastructure. Focus on multi-model strategies, cost optimization, and performance monitoring rather than betting everything on a single provider.

Layer 2: Protocol Layer (Infrastructure Advantage)

What It Is: Standardized protocols for AI-to-tool communication, including MCP (Model Context Protocol), function calling specifications, and tool discovery mechanisms.

Moat Potential: MEDIUM

Protocol layers can create network effects when widely adopted. Companies that establish protocol standards or contribute valuable infrastructure can build gravitational pull within the ecosystem.

Strategic Opportunities:

• Contributing high-quality protocol implementations that others depend on

• Building ecosystem network effects through developer adoption

• Establishing technical leadership in emerging standards

Business Implications: Early protocol leadership can create lasting advantages, but these require significant ecosystem adoption to become defensible. Focus on developer experience and community building.

Layer 2: Protocol Layer (Infrastructure Advantage)

What It Is: Standardized protocols for AI-to-tool communication, including MCP (Model Context Protocol), function calling specifications, and tool discovery mechanisms.

Moat Potential: MEDIUM

Protocol layers can create network effects when widely adopted. Companies that establish protocol standards or contribute valuable infrastructure can build gravitational pull within the ecosystem.

Strategic Opportunities:

• Contributing high-quality protocol implementations that others depend on

• Building ecosystem network effects through developer adoption

• Establishing technical leadership in emerging standards

Business Implications: Early protocol leadership can create lasting advantages, but these require significant ecosystem adoption to become defensible. Focus on developer experience and community building.

Layer 3: Context Engineering (The Science & Art)

What It Is: The sophisticated orchestration of information to enable AI reasoning, including information architecture, retrieval-augmented generation (RAG), memory management, and context optimization.

Moat Potential: HIGH

Context engineering represents the first layer where deep technical expertise creates significant differentiation. The ability to architect information flows that consistently produce superior AI performance requires specialized knowledge that's difficult to replicate.

Strategic Advantages:

• Information Architecture Mastery: Understanding how to structure and prioritize information for optimal AI performance

• Model Psychology: Intuitive knowledge of how different models process and attend to information

• Performance Optimization: Ability to achieve superior results with the same underlying models through better context engineering

Business Implications: This layer rewards deep technical expertise and experimentation. Companies that master context engineering can achieve dramatically better performance than competitors using the same foundation models.

Layer 4: Agent Orchestration (Complex System Coordination)

What It Is: Multi-agent coordination systems, workflow engines, state management across agents, and complex system orchestration that enables sophisticated autonomous behaviors.

Moat Potential: VERY HIGH

Agent orchestration requires solving complex distributed systems problems while maintaining reliability and performance. The architectural patterns and coordination logic become increasingly sophisticated and difficult to replicate.

Strategic Advantages:

• System Complexity Management: Ability to coordinate multiple agents reliably at scale

• Reliability Patterns: Proven approaches to handling failures and edge cases

• Performance Architecture: Optimized systems that maintain speed and accuracy under load

• Coordination Logic: Sophisticated decision-making frameworks for agent interaction

Business Implications: This layer heavily favors companies with strong systems engineering capabilities. The complexity creates natural barriers to entry and significant switching costs.

Layer 5: Application Logic (Industry-Specific Intelligence)

What It Is: Business rules, domain-specific logic, industry workflows, and specialized knowledge that makes AI useful for particular verticals or use cases.

Moat Potential: HIGH

Application logic represents the intersection of AI capabilities with real-world business needs. Deep domain expertise creates significant advantages that are difficult for generalist competitors to replicate.

Strategic Advantages:

• Domain Knowledge: Deep understanding of industry-specific workflows, regulations, and requirements

• Business Process Integration: Ability to embed AI into existing business operations seamlessly

• Workflow Optimization: Industry-specific patterns that maximize AI value

• Regulatory Compliance: Understanding of industry-specific legal and compliance requirements

Business Implications: Vertical specialization becomes increasingly valuable. Companies that build deep domain expertise can command premium pricing and create strong customer relationships.

Layer 6: User Experience (Human-AI Interaction Mastery)

What It Is: Interface design, interaction patterns, user psychology understanding, and the orchestration of human-AI collaboration that makes powerful AI capabilities accessible and delightful.

Moat Potential: MEDIUM

User experience can create strong brand loyalty and adoption advantages, but design patterns tend to converge over time as best practices emerge across the industry.

Strategic Advantages:

• Design Excellence: Superior interface design that makes complex AI capabilities feel simple

• User Psychology: Understanding how people naturally want to interact with AI

• Interaction Patterns: Proven approaches to human-AI collaboration

• Brand Loyalty: Strong user relationships built through superior experience

Business Implications: UX advantages can drive early adoption and user retention, but require continuous innovation to maintain differentiation as the market matures.

Layer 7: Security & Governance (Trust & Control Framework)

What It Is: Compliance frameworks, audit trails, permission models, data sovereignty controls, and governance systems that enable safe AI deployment at scale.

Moat Potential: VERY HIGH

Security and governance represent critical requirements for enterprise adoption, and building comprehensive trust frameworks requires deep expertise and significant time investment.

Strategic Advantages:

• Compliance Expertise: Deep knowledge of regulatory requirements across industries

• Security Architecture: Proven security patterns for AI systems

• Trust Relationships: Established credibility with enterprise security teams

• Regulatory Barriers: Compliance capabilities that create barriers for new entrants

Business Implications: Security leadership can become a significant competitive advantage, especially in regulated industries. These capabilities take years to build and are extremely difficult to replicate.

Layer 8: Data & Integration (Proprietary Data Assets)

What It Is: Customer data accumulation, system integrations, proprietary datasets, and the context that accumulates through usage, creating increasingly valuable and unique information assets.

Moat Potential: VERY HIGH

Data and integration layers create the strongest moats through network effects, switching costs, and proprietary information accumulation that improves AI performance over time.

Strategic Advantages:

• Data Network Effects: More usage creates better AI performance, attracting more users

• Integration Lock-in: Deep system integrations that are expensive and risky to replace

• Context Accumulation: Proprietary understanding of customer needs and behaviors

• Switching Costs: High costs associated with moving data and reconfiguring integrations

Business Implications: Data advantages compound over time and become extremely difficult for competitors to replicate. Focus on creating valuable data flywheels from day one.

Strategic Implications by Layer

Highest Moat Potential: Focus Areas for Long-Term Advantage

Data & Integration • Security & Governance • Agent Orchestration

These layers offer the strongest potential for building sustainable competitive advantages. They require deep technical expertise, significant time investment, and create compounding advantages over time.

Strategic Focus: Invest heavily in these capabilities early. Build expertise that will be difficult for competitors to replicate. Timeline: 2-5 years to build truly defensible positions.

Execution Approach: Hire senior technical talent, invest in R&D, and focus on creating proprietary advantages that compound over time.

Medium Moat Potential: Skill-Based Differentiation

Context Engineering • Application Logic • User Experience

These layers reward specialized skills and domain expertise. While defensible, they require continuous innovation and improvement to maintain advantages.

Strategic Focus: Build specialized expertise and domain knowledge. Create centers of excellence around these capabilities. Timeline: 6-18 months to build defensible positions.

Execution Approach: Invest in specialized talent, continuous learning, and rapid iteration to stay ahead of competitors.

Lowest Moat Potential: Efficiency and Execution Focus

Foundation Models • Protocol Layer

These layers are rapidly commoditizing. Success depends on execution speed, cost efficiency, and ecosystem participation rather than proprietary advantages.

Strategic Focus: Optimize for speed and efficiency. Participate in ecosystems rather than trying to control them. Timeline: Weeks to implement, easily commoditized.

Execution Approach: Focus on operational excellence, cost optimization, and fast implementation of emerging standards.

The Layer Selection Strategy

For Early-Stage Companies

• Start with Application Logic and Context Engineering: These layers offer good moat potential while being achievable for smaller teams. Build deep domain expertise in specific verticals while developing sophisticated context engineering capabilities.

• Avoid Foundation Model Dependence: Don't build strategies that depend on exclusive access to particular models. Instead, focus on capabilities that work across multiple foundation models.

• Plan for Data Accumulation: Design systems from day one to accumulate valuable data and create network effects over time.

For Growth-Stage Companies

• Invest in Security & Governance: As you target enterprise customers, security capabilities become table stakes and significant differentiators.

• Build Agent Orchestration Capabilities: Develop sophisticated multi-agent systems that can handle complex workflows reliably at scale.

• Deepen Integration Moats: Create increasingly valuable integrations that raise switching costs for customers.

For Enterprise Companies

• Dominate Data & Integration: Focus on creating the strongest possible data network effects and integration lock-in.

• Lead in Security & Governance: Become the trusted standard for secure AI deployment in your target markets.

• Build Platform Ecosystems: Use your accumulated advantages to create platforms that others build on top of.

Common Strategic Mistakes

The Foundation Model Trap

Building entire strategies around access to particular foundation models. These advantages disappear quickly as models commoditize and new providers emerge.

The Feature Parity Race

Competing on feature breadth rather than depth. This leads to shallow implementations across multiple layers rather than deep advantages in specific areas.

The Layer Confusion

Trying to build moats at every layer simultaneously. This dilutes focus and prevents building truly defensible positions anywhere.

The Timing Misjudgment

Investing in layer capabilities too early (before market readiness) or too late (after commoditization). Understanding the maturity curve of each layer is crucial.

Layer-Specific Moat Building

The agentic AI stack is not monolithic. Each layer presents different opportunities, challenges, and timelines for building competitive advantages. Success requires understanding these dynamics and focusing resources where sustainable moats can be built.

The companies that win will be those that choose their layers strategically, invest appropriately in each, and build compounding advantages over time. They'll avoid the commoditized layers while building deep expertise in the high-value areas where true differentiation is possible.

The future belongs to companies that understand the stack, choose their battles wisely, and execute with focus and precision. The question isn't whether to build in the agentic AI space—it's where in the stack to build, and how to create advantages that compound over time.

In this new landscape, layer selection becomes strategy, and strategy becomes sustainable competitive advantage.

Recap: In This Issue!

1. Foundation Models (Low Moat)

• Commoditized layer: GPT-4, Claude, Gemini, etc.

• Strategic Advice: Treat as infrastructure; optimize for performance/cost. Don't build differentiation here.

2. Protocol Layer (Medium Moat)

• Standardization tools: MCP, function calling, tool discovery.

• Strategic Advice: Participate early in emerging standards; build dev-friendly protocols to gain network effects.

3. Context Engineering (High Moat)

• Sophisticated prompt + data orchestration (RAG, memory, architecture).

• Strategic Advice: Master model psychology, information structuring, and performance tuning to gain edge.

4. Agent Orchestration (Very High Moat)

• Multi-agent systems, workflow engines, stateful coordination.

• Strategic Advice: Invest in systems engineering; creates hard-to-replicate barriers through complexity.

5. Application Logic (High Moat)

• Vertical expertise: domain-specific workflows, regulations, and rules.

• Strategic Advice: Embed deeply in industry use cases. Build defensible solutions through real-world integration.

6. User Experience (UX) (Medium Moat)

• Human-AI interaction, design patterns, usability.

• Strategic Advice: Great UX wins early adopters and retention but must evolve continuously to maintain lead.

7. Security & Governance (Very High Moat)

• Enterprise-critical: compliance, auditability, trust frameworks.

• Strategic Advice: Become a trusted player in regulated markets; moat grows with trust and track record.

8. Data & Integration (Very High Moat)

• Proprietary data, integration depth, switching costs.

• Strategic Advice: Build data flywheels and deep integrations. Moat compounds with usage and time

Layer Selection by Company Stage

• Early Stage:Focus on Context Engineering + Application Logic for fast iteration and achievable moats.Begin data accumulation from Day 1.

• Growth Stage:Invest in Security & Governance for enterprise readiness.Develop Agent Orchestration and deepen integrations.

• Enterprise Stage:Dominate Data & Integration.Create platform ecosystems and trusted standards.

Common Mistakes to Avoid

• Foundation Model Trap: Betting on exclusive model access—short-lived advantage.

• Feature Parity Race: Spreading too thin across layers; no depth = no moat.

• Layer Confusion: Trying to build everything at once = diluted focus.

• Timing Errors: Misjudging market maturity—either too early or too late

  
  

  Layer selection is a strategy; the winners in the agentic AI era will choose the right layers, build compoundable moats, and execute with focused precision.