The world of AI applications is changing rapidly. Not too long ago, most AI systems were simple: a single model received input, made a prediction, and returned a result. But now, with the rise of Large Language Models (LLMs) and techniques like Retrieval Augmented Generation (RAG), AI applications are becoming far more dynamic, context-aware, and interactive. These advances have led developers to rethink how they design the software architectures that power modern AI solutions. We refer to the original article presented in 2023 by a16z to review the change.
Co-written with: Gad Benram, Diogo Gonçalves, Bruno Alho, Gabriel Gonçalves
In this post, we’ll explore how the introduction of RAG into agent-based (or “agentic”) workflows has changed the landscape of LLM applications. We’ll look at a new kind of AI application architecture that weaves together different components—such as specialized data stores, orchestration layers, model management tools, and safety systems—to deliver richer, safer, and more personalized user experiences. We’ll also ask some leading questions to help guide you as you design your own solutions.
Why the Architecture of AI Applications is Changing
1. More Powerful Models: Large Language Models have evolved from simple chatbots into general-purpose reasoning engines. The release of the o1 model marks a significant leap from conversational models like Llama3 and GPT-4o to those capable of advanced reasoning. These models excel at planning and supporting complex agentic workflows, offering a glimpse into AI applications that can organize themselves into social-like structures.
2. Agentic Workflows: Rather than just generating a single answer, modern AI “agents” can break problems into steps, use APIs to gather information, verify their reasoning, and then produce a result. Tools like Google Vertex AI Agent Builder or frameworks like LangChain help developers build these complex reasoning chains. This agentic approach demands a runtime environment designed to handle multiple steps, checks, and external tool calls.
A Modern AI Architecture: Key Components
Picture a modern AI system as a set of specialized building blocks that work together. Let’s give them simple, understandable names. You can adapt these names to suit your own branding or tool choices.
1. Knowledge and Context Layer
Dynamic Context Retriever (RAG Engine): Imagine your AI assistant needs the latest financial reports or a summary of recent news articles. A RAG engine uses vector databases (like Pinecone or Chroma) to find semantically similar information. It can also use graph databases (like Neo4j) to understand relationships between entities (people, places, products) or OpenSearch and ElasticSearch to handle traditional keyword searches.
2. User Interaction Channels
Conversational Interfaces (Chat, Voice, Video): Users might interact with your AI system through a chat widget on a website, a voice assistant on a smartphone, or even video-based interactions. These interactions typically involve a UI frontend component, along with a chat framework like LangChain, which helps maintain historical messages and ensures a consistent conversation with the user.
Multi-Modal Interfaces: With vision transformers, your AI could look at a product design mock-up and suggest improvements, or review a diagram and explain it in simple terms.
3. Agent Runtime Environment
AI Agent Orchestration (e.g., Vertex AI Agent Builder): This layer manages the “chain-of-thought” of your AI agents. It decides when to query data sources, when to call external APIs, and how to validate intermediate results. This orchestration ensures the agent can handle complex tasks, not just simple Q&A.
4. Model Management and Generation
Model Factory (Continuous Model Lifecycle): Modern AI systems often rely on multiple models—some large, some small, some specialized. A Model Factory is where you generate, tune, validate, and deploy these models. Tools like Hugging Face Hub, Vertex AI and SageMaker, or custom pipelines help ensure you always have the right model running in production.
Foundation and Specialized Models (LLMs, Vision Transformers):Your architecture might include a general-purpose LLM (like GPT-4o), a vision transformer (like ViT or CLIP), and domain-specific models (like a code completion model or a medical diagnosis model).
5. Prompt Management and LLMOps
Prompt Engineering Tools: Good prompts lead to reliable, useful answers. This involves carefully crafting the instructions you give to your LLM. Prompt templates, tools like PromptLayer, and configuration files make it easier to manage prompts at scale.
6. AI Safety, Guardrails, and Governance
Safety Filters and Policy Layers: Ensuring that an AI behaves responsibly is critical. Safety layers can block harmful, biased, or confidential information from being shared. Tools like OpenAI’s moderation API help maintain ethical standards.
Explainability and Auditing:You might include dashboards that show how the AI arrived at an answer, or logs that auditors can review later. This builds trust and helps you catch issues before they become major problems.
7. Connectivity and Routing Layer
AI Proxy / Gateway: Think of this as the “traffic control center” for your AI system. When requests come in, the gateway decides which model or data source to use. As you add more models and services, a flexible gateway can help route requests efficiently and safely.
Putting It All Together: A Roadmap for the Future
As you design modern AI applications, you’re not just dealing with a single model. Instead, you’re orchestrating a symphony of components: data retrieval systems, LLMs, safety filters, user interfaces, and more. The key is to think modularly. Each part—data sources, model management, orchestration, safety, and connectivity—can be chosen, upgraded, and swapped out as your needs evolve.
Conclusion
We’re entering a new era of AI application design. By incorporating RAG workflows, agentic reasoning, and modular architectures, we can build systems that are not only more intelligent but also more resilient, explainable, and trustworthy. As you plan your next AI project, consider how these architectural components can help you create a powerful, safe, and user-friendly AI experience. Each building block you add provides a stepping stone toward a future where AI doesn’t just respond to questions—but truly understands, adapts, and supports users in meaningful ways.
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