ИИ агент: архитектура, технологии, инструметры

What's under the hood and what tools do developers use?

AI agents have become the central trend of 2024-2025. They are no longer perceived as "smart chatbots", but act as full-fledged performers of tasks: they collect data, process documents, manage APIs, run scripts, aggregate reports, communicate with customers, monitor order statuses.

In order for an agent to work as a live employee, a well—thought-out technical architecture is required. Let's look at what's under the hood and what tools developers use.

Architecture of a modern AI agent

An AI agent is not just one model. This is a bundle of modules, each of which performs its own role.

LLM core (model of thinking)

• understanding queries and context,

• The reasoning,

• Decision-making,

• adjusting the task execution strategy.

High—quality systems use not one LLM, but several: one for analysis, another for text generation, and the third for cheap auxiliary tasks.

Tools (tools, actions)

The agent must be able to act. To do this, he is given a set of tools.:

• API calls,

• database queries,

• functions (in the spirit of OpenAI "function calling"),

• running scripts,

• performing CRUD operations,

• browser management,

• working with files.

The LLM "decides" which tool to use and forms the call structure.

Memory (Memory)

The agent must remember:

• status of the current task,

• communication history,

• user data,

• progress of the execution.

Technically, memory is implemented via:

• vector databases (Weaviate, Qdrant, Pinecone),

• Redis как short-term memory,

• knowledge repository (PostgreSQL/MySQL).

Context (Context Orchestrator)

A module that collects everything the agent needs for reflection.:

• recent history,

• external documents,

• instrument results,

• user data.

At this stage, the RAG (Retrieval-Augmented Generation) mechanisms are working.

Planner (Planner / Reasoner)

The following are used here:

• chain-of-thought,

• tree-of-thought,

• agentic workflow,

• analytical models (division of roles into “think”/“act").

The Executor (Executor)

The one who really launches:

• requests,

• Python scripts,

• integrations,

• SQL,

• automation of actions.

Executor provides security (sandboxing), timeouts, and error control.

What technologies do developers use?

The agent's core

• DeepSeek R1 / DeepSeek V3

• OpenAI GPT-4.1 / GPT-5.1

• Anthropic Claude 3.5 Sonnet / Opus

• YandexGPT 4 / YaLM 3

• Mistral Large / Small

RAG and memory

• Qdrant (open-source, actively used in Russia)

• Weaviate

• Pinecone

• Redis Stack for in-memory search

Tool management

• OpenAI Function Calling

• Mistral Tools

• YandexGPT Functions

• LangChain Tools

• LlamaIndex Agents (including AgentRunner)

Agent Orchestration

• LangChain

• LlamaIndex

• OpenAI Assistants API

• FastAPI + Python tools

• N8N / Airflow

Browser management and actions

• Playwright

• Puppeteer

• Selenium

With their help, an agent can visit the website, fill out a form, download documents, and check analytics.

Integrations and APIs

• REST / GraphQL

• gRPC

• Webhooks

• The company's internal API

How an AI agent is created technically

Step 1. Defining tasks

What actions should an agent be able to do: analyze documents? Process a request to CRM?

Step 2. Collecting tools

The developer creates functions:

• getOrders(),

• updateLead(),

• runSQL(),

• sendEmail(),

• getAnalytics().

And describes their structure for LLM.

Step 3. Setting up the memory

Being created:

• vector base,

• database for long-term memory,

• the context manager.

Step 4. Model Configuration

Specified:

• the model of thinking,

• generation model,

• temperature conditions,

• the size of the context.

Step 5. Create a Scheduler

This is where the agent learns to break down the task into steps.

Optionally, they are connected:

• Traces of reflections (CoT),

• The plan tree,

• interim reports.

Step 6. Integration and Testing

The agent connects to real systems:

• CRM,

• ERP,

• Telegram bots,

• databases,

• the internal API.

It is then tested on real-world scenarios.

Ready-made platforms for creating AI agents

If you don't want to write your own framework, there are ready-made solutions.

Platforms for business

• OpenAI Assistants

• Anthropic Workflows (2025)

• YandexGPT Agents

• Mistral Agents

• DeepSeek Agents Framework (beta)

Tools for developers

• LangChain

• LlamaIndex

• FastAPI / Django / Laravel + LLM SDK

• Airflow / Temporal / N8N

• Dify.ai (no-code + ability to write custom functions)

• Flowise / LangFlow

The main technical principles by which the agent works

1. The LLM makes decisions.

2. The tools give the agent hands.

3. Memory provides a long-term context.

4. RAG provides access to knowledge outside of LLM.

5. The orchestrator manages the steps and coordinates the instruments.

6. The Executor does the real work.

7. Security is a mandatory layer (limits, sandbox, logging).

The Future of AI Agents: 2025-2026

We will see growth in the coming years.:

• manage complex processes

• multimodal agents (text + voice + video + actions);

• corporate systems that take control of entire blocks of business operations: finance, sales, logistics;

• self-improving-adents are systems that learn from their own mistakes and improve themselves without a developer.

Agents will no longer be a toy. They will become part of the company's IT infrastructure, such as APIs, servers, and ERP.