A comprehensive approach to building and managing AI systems can be structured into the following five distinct layers
1. Infrastructure Layer: Core Computational Power
At the base of the stack, the Infrastructure Layer handles the most demanding computational tasks. It is responsible for running complex models, managing large-scale data storage, and ensuring seamless scalability of workloads. A key function of this layer is to provide the power required to host a large language model serving thousands of queries per hour.
Leading Platforms: OpenAI, Hugging Face, Mistral, Anthropic
2. Intelligence Layer: Advanced Cognitive Functions
This layer augments the raw capabilities of AI models by integrating memory, reasoning frameworks, and retrieval mechanisms. It enables the creation of sophisticated systems, such as a RAG-powered financial assistant capable of accessing and referencing specific corporate reports upon request.
Enabling Technologies: LangChain, LlamaIndex, Pinecone
3. Engineering Layer: From Prototype to Production
The Engineering Layer provides the tools and processes necessary to convert experimental AI models into fast, reliable, and scalable products. It bridges the gap between a simple proof-of-concept, like a notebook-based chatbot, and a production-ready application capable of supporting a large user base.
Deployment and Scaling Tools: Lamini, Relevance AI, Modal
4. Observability Layer: Ensuring AI Integrity and Safety
This critical layer is dedicated to monitoring, evaluating, and governing AI behavior. Its primary function is to ensure that AI systems operate in an ethical, accurate, and safe manner. For example, it can be used to identify and flag hallucinations in a medical diagnostic tool before it is used in a real-world clinical setting.
Monitoring and Governance Solutions: WhyLabs, Guardrails AI, Lakera
5. Agent Layer: The Human-AI Interface
The Agent Layer is where users interact with the AI system. This is accomplished through various interfaces, including copilots, virtual assistants, and autonomous agents that perform tasks on behalf of the user. A well-known example is GitHub Copilot, which integrates directly into a developer's workflow to autocomplete code and suggest solutions.
User-Facing Applications: Cursor, GitHub Copilot, Cognition