Technical stack of AI agents: LLM, orchestration, vector memory, and tools

An AI-agent is not just a call to a language model. Behind every autonomous agent lies a multi-layered architecture, and the choice of each component determines how reliably and predictably the system handles real-world tasks.

Layers of the agent stack

At the foundation is a language model — GPT-4o, Claude 3.5 Sonnet, Gemini 1.5, or open alternatives like Llama 3 and Mistral. It is responsible for reasoning. The choice of model determines the ceiling of the agent's capabilities: a powerful model handles multi-step tasks better, but is more expensive and slower to run.

Above the LLM sits the orchestration layer — a framework or custom code that manages the cycle "think → select tool → execute → evaluate → continue". The most common frameworks are: LangChain (rich integration ecosystem), LlamaIndex (focus on RAG and data work), AutoGen by Microsoft (dialogue between multiple agents), CrewAI (agents with roles and teamwork). Each balances flexibility and configuration complexity differently. For production solutions, orchestration is increasingly written from scratch — this makes it easier to control agent behavior at each step.

Memory: from tokens to vector databases

An agent without memory drops all context after each conversation. Short-term memory is the context window of the current session: everything that fits in tokens, the model "remembers" right now. But the window is finite, expensive to keep everything in it, and it quickly fills up during long sessions.

Long-term memory is implemented through vector databases: Pinecone, Chroma, Weaviate, Qdrant, pgvector. The agent vectorizes facts and stores them, then retrieves them via semantic search when needed. This is how RAG (Retrieval-Augmented Generation) works: instead of storing all context in tokens, the system queries only what is relevant for a particular step. This reduces cost and decreases the likelihood of hallucinations.

The third level is semantic caching: if the agent has already answered a similar query, the system returns the cached result without a new LLM call. In production scenarios with recurring patterns, this noticeably reduces latency and infrastructure cost.

Tools and actions

Tools transform an agent from "smart chat" into a system that actually does something. Without them, an agent is limited to knowledge from training data, which quickly becomes outdated. A typical set in a production agent:

• Real-time web search (Brave Search, Tavily, SerpAPI)
• Code execution (Python REPL, E2B Sandbox)
• Working with files, tables, PDFs, and databases
• HTTP requests to external APIs and corporate services
• Browser automation (Playwright, Puppeteer)

The linking piece is function calling: the model describes which tool to call and with what arguments, the orchestrator executes the call and returns the result to the context. The cycle "think — act — observe" repeats until the task is completed or human intervention is needed.

A separate and often underestimated component is observability. In production, it's important to understand why the agent made a particular decision and where it failed. Tracing tools like LangSmith or Langfuse capture every step, allow you to compare prompt versions, and measure answer quality.

What this means

The technical stack of an AI-agent is a set of concrete engineering tradeoffs, not an abstraction. The right choice of framework, memory layer, and tools determines whether the agent will be reliable in production or will hallucinate and hang midway through a goal. As agent systems move from laboratories into real products, understanding each layer of the stack becomes a foundational skill for AI application developers.