Feature
The explosion in use of generative AI tools has left many developers, businesses and curious technologists wondering what’s under the hood. Behind every AI assistant that writes, recommends, summarizes or chats lies a modern AI stack — a multi-layered architecture built to handle everything from data ingestion to real-time decision-making.
Understanding how these layers work together is essential for anyone trying to move beyond simple prompts and build AI applications that scale. According to Arun Chandrasekaran, vice president, analyst at Gartner, the genAI landscape consists of four critical layers: infrastructure, models, engineering tools and applications & agents.
At the heart of this architecture are foundation models, which Chandrasekaran said “continue to be an important driver of innovation and progress in this ecosystem.” Their versatility enables a wide range of tasks, from language generation to summarization, and their launch heralded the real revolution in the genAI space.
Table of Contents
- Orchestration: The Hidden Glue of the AI Stack
- Memory: From Context to Personalization
- Vector Databases: The Backbone of RAG
- User Experience: Trust & Transparency in the Stack
- Bringing It All Together: The Modern AI Stack in Action
Orchestration: The Hidden Glue of the AI Stack
Foundation models may do the heavy lifting, but they don’t act alone.
“On their own, they’re stateless and unaware of your business logic or tools,” said Shrianth Thube, IEEE senior member. “That’s where orchestration comes in to manage workflows, route steps, add memory and tie into APIs or databases.”
This orchestration layer — where tools like LangChain and Dust shine — forms the connective tissue between AI models and the rest of the stack. Orchestration frameworks provide an abstraction layer to enable:
- Prompt chaining
- Model chaining
- Interfacing with external APIs
- Retrieving contextual data
- Maintaining statefulness
These tools are key to improving model accuracy, particularly by searching and summarizing corporate data, and integrating that data into AI outputs in a structured and consistent way.
Yet orchestration isn’t without challenges; Thube noted that while frameworks like LangChain make it easy to prototype, they can become difficult to manage.
“However, as soon as the workflows get larger — like chaining multiple tools, calling APIs or tracking memory — the system becomes hard to debug and maintain,” he said.
He also pointed to performance issues and limited observability. “There’s often no easy way to see which step failed, or what context was passed between components. For production systems, teams end up needing to write custom logging and monitoring just to keep the system stable.”
Related Article: The 3 Elements Every AI-Driven Tech Stack Needs to Compete
Memory: From Context to Personalization
Another critical piece of the modern AI stack is memory — both short-term and long-term.
“Memory in AI systems stores information from past interactions,” said Chandrasekaran. “Developers use it to improve user experiences by tracking conversation history, preferences and goals.”
Thube explained memory makes AI apps feel smart and personal. “Without memory, each prompt is treated as a one-time thing. That breaks down fast when you’re building assistants, chat tools or anything that spans multiple steps.”
According to Thube, different memory types serve different purposes:
- Short-Term Memory: Holds immediate context
- Long-Term Memory: Retains facts or preferences
- Retrieval Memory: Typically powered by vector databases; enables models to access relevant documents or structured data on demand
Yet memory must be handled carefully.
“If you store everything, it slows things down and may confuse the model,” said Thube. “If you store too little, the user gets a disconnected experience.” The key is balancing summarization, filtering and update strategies to maintain relevance and performance.
Vector Databases: The Backbone of RAG
Vector databases themselves represent a vital evolution in how AI systems retrieve knowledge.
Chandrasekaran described them as “databases that store vector embeddings” and are responsible for “similarity search that finds the best match between the user’s prompt and the data.” They help GenAI applications deliver low-latency responses at scale and improve accuracy through retrieval-augmented generation (RAG).
“Vector databases are essential in RAG setups,” Thube agreed. “They let you search by meaning instead of keywords… It’s what makes RAG a practical option in enterprise settings, especially when you’re working with live data or proprietary knowledge.”
User Experience: Trust & Transparency in the Stack
Finally, the user interface — the front-end of the AI stack — plays a critical role in whether these complex systems work in practice.
Chandrasekaran said prompt engineering tools are becoming a primary method of steering frozen models, using “context, examples and data retrieval” to deliver desired outcomes. He also pointed to emerging concepts like “vibe coding” and “context engineering” that give developers more control over the data and tools models can access.
Thube emphasized the importance of clear UX and transparency.
“Keep it simple, but make sure the user understands what’s happening behind the scenes. People should know where answers are coming from, what the AI can and can’t do and how to adjust if something is wrong.”
Building real products — not just prototypes — means adding helpful cues, clear feedback and source references to create trust and usability.
Webinar
Sep
25
CMS Briefing: A Live Look at What’s Next in AI-Driven Platforms
Learn how leading organizations are using AI‑driven tools to publish faster, personalize smarter and stay secure.
Register
Webinar
On demand
Ready or Not: How Data-First Organizations Are Unlocking Agentforce Potential
Learn how to cut through the noise, activate Agentforce and build a Salesforce AI strategy that actually delivers.
Watch Now
Webinar
On demand
AI in Customer Service: Faster Resolutions, Happier Customers
Don’t let rising demand burn out your team. See how to build a smarter, more resilient support org.
Watch Now
Webinar
On demand
From Hype to High-Impact CX Strategies That Actually Scale
Turn buzzworthy AI and outsourcing trends into measurable CX wins with fresh 2025 data.
Watch Now
Webinar
On demand
Insights to Action Rethinking the Contact Center for Real Business Impact
Join our exclusive webinar to hear CX executives share their innovative strategies for transforming service delivery.
Watch Now
Webinar
On demand
Accelerating Healthcare Ops with AI
Sign up to see how your peers are putting AI to work — and making it stick.
Watch Now
Related Article: The AI Revolution, Part 1: Drawing the Lines That Will Define the Future of AI
Bringing It All Together: The Modern AI Stack in Action
As more organizations move from experimentation to implementation, the importance of understanding — and tuning — each layer of the AI stack will only grow. From the intelligence of foundation models to the retrieval power of vector databases and the orchestration that holds it all together, the modern AI stack is a finely layered system of components working in concert.
“The real magic happens in how you combine these pieces,” according to Thube. “It’s not about having one great model — it’s about how you stitch it together to build something useful.”
