A recent analysis of the AI agents stack reveals a dramatic shift in the infrastructure, demonstrating how development teams can handle the complexities of agent architecture by understanding the unique layers that have emerged since 2024. The evolution of these frameworks goes beyond a technical update; it highlights the distinct requirements for building stable AI agents.
In November 2024, Letta introduced a foundational diagram that became a key reference for engineering teams, mapping the essential layers of AI agent architecture. Fast forward to 2026, and that diagram has evolved significantly, with six layers now identified, many of which were not previously considered. The introduction of the Model Context Protocol (MCP) standardized tool connectivity, while advancements in memory management and reasoning models have redefined agent capabilities.
Mapping the Six Layers of AI Agents
The architecture of AI agents is not merely an extension of LLM (Large Language Model) stacks; it represents a unique set of challenges and requirements. Agents need to manage state, access tools through defined protocols, and maintain persistent memory across interactions. The six recognized layers include:
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Models and Inference: At the core of the agent's functionality is the model responsible for executing tasks. As reasoning models like Claude and DeepSeek develop, the line between simple API calls and complex problem-solving capabilities has blurred. The trend is shifting towards open weight models, which have significantly improved in quality, allowing teams to prototype effectively before deploying in production.
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Protocols and Tools: The emergence of MCP has redefined how agents connect with external tools and APIs, leading to a more standardized approach. With MCP now adopted by major players like OpenAI and Google, the focus shifts to security and the need for stable protocols to prevent vulnerabilities in tool usage.
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Memory and Knowledge: Initially treated as an afterthought, memory now plays a critical role in agent functionality. The 2026 architecture recognizes memory as a fundamental component, offering multiple tiers for storing and retrieving knowledge. This evolution emphasizes the importance of context management over traditional prompt engineering.
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Frameworks and SDKs: The landscape has diversified with numerous providers now offering SDKs for agent development. Teams face choices between using built-in SDKs for quick deployments or opting for more complex graph-based frameworks that provide greater flexibility but require additional setup.
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Eval and Observability: With the rise of production agents, the need for stable evaluation metrics has become clear. Many teams now use observability tools to continuously monitor agent performance, an essential practice to identify and rectify issues before they impact users.
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Guardrails and Safety: As agents take on more responsibilities, ensuring they operate within defined constraints is vital. The guardrails for agents have evolved to include real-time monitoring and authorization protocols, reflecting the increased complexity of their tasks.
Implications for Development Teams
For development teams, navigating this evolving architecture requires a clear understanding of how to evaluate and implement each layer effectively. Key considerations include the degree of state management required, the potential for vendor lock-in, and the complexity of transitioning from prototype to production.
The current landscape illustrates a tendency for teams to overbuild or misconfigure their agent architecture. Many are incorporating advanced features without fully understanding their needs, often resulting in inefficient systems. For example, building a multi-agent system without first establishing a functional single-agent architecture can lead to unnecessary complications.
Teams are encouraged to adopt a more strategic approach: start simple with foundational layers and incrementally add complexity as specific challenges arise. By aligning their architecture with the requirements of their intended agent type—whether a basic tool caller or a sophisticated multi-agent system—they can avoid pitfalls that have plagued earlier implementations.
Looking Ahead
As we approach 2027, the trajectory indicates that many teams will gravitate toward integrated solutions offered by model providers, streamlining the development process. However, the need for custom solutions in intricate scenarios will persist, particularly as agents scale and evolve in complexity. Understanding which layers to prioritize will remain crucial, as will the ability to identify and address failures within the architecture.
The AI agents stack serves as a reminder of the rapid advancements in the field and the necessity for development teams to adapt their strategies accordingly. As the landscape continues to change, those who can effectively navigate these layers will be better positioned to build successful, resilient AI agents.
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