AI agents: a complete guide to autonomous neural networks for beginners
The first prototype of an AI agent was created by Arthur Samuel in 1952. He created the first self-learning computer that plays checkers. Arthur Samuel perfected the computer system, and already in 1956 the machine won the American checkers champion, Walter Hellman.
What are AI agents in simple words
AI agents are proactive programs based on artificial intelligence. They independently make decisions, take actions to achieve their goals, and interact with the external environment without human intervention.
Imagine a chef who comes up with a seasonal menu. He does not wait for instructions and explanations, but develops the concept of the dishes himself, makes a list of ingredients, buys them and starts experimenting.
This is also how an AI agent works - it is able to analyze, collect the necessary information and make decisions.
Key characteristics of AI agents
Artificial intelligence agents are not just chatbots working on command. It is a system of autonomous task execution to achieve a goal.
The difference between such programs is as follows:
- Autonomy.
One of the advantages of AI agents is that they work without constant human involvement. Formulate a task, and the agent will choose its own steps. It will not wait for commands, but will act as an independent employee. This frees up time for other important tasks.
- Adaptability.
AI agents are convenient because they work with feedback. They learn and adapt to new conditions. This flexibility improves the performance of the AI tool and the result obtained.
- Goal-setting.
An agent, like a person, reacts not only to a command, but also focuses on the end result. If the goal is to "increase the number of subscriptions to the newsletter", he will not limit himself to one tool, but will try different ones: improve the text, test variants, offer a new call-to-action.
- Interaction with the environment.
Another one of the benefits is integration into a company's work environment. AI agents easily work with files on the computer, with the calendar, with CRM or sensors. Working with external data makes it possible to make relevant decisions based on up-to-date information.
How an AI agent differs from an AI assistant
AI assistant and AI agent are similar concepts but differ in their working principles. AI-assistant and AI-agent comparison table:
Criterion | AI assistant | AI agent |
Principle of operation | Works on demand | Independently makes decisions for a given purpose |
Behavior | Executes specific commands | Analyzes the external environment to adopt a suitable option |
Adaptability | Personalizes responses based on data, but does not change behavioral strategy | Changes behavioral strategy based on experience and new data |
Interaction with external systems | Integrates with CRM systems, cloud storage, mail, payment system. But it operates according to a set scenario | Uses external data to make independent decisions |
Examples | Siri, Alice from Yandex, smart home systems, ChatGPT, GigaChat | Unmanned vehicles, robotics, algotrading |
The table shows that an agent is a full-fledged task performer who receives a goal, analyzes, makes decisions and plans an action. To improve performance - adapts and takes into account past experience.
Examples of using AI agents in business
The AI agent is introduced in the company on a par with other AI tools. Working with AI makes it possible to relieve the workload of employees, optimize and speed up work processes, save resources, and increase efficiency. Their introduction is possible in various industries: from freelancing to manufacturing companies.
For freelancers and marketers.
Automating routine tasks frees up time for client work, creating logos, banners, texts, new products.
AI agents:
- Analyze statistics and target audience;
- Finding substandard elements in the work;
- Suggest improvements or replacements;
- Identify promotional channels.
In Russia, agents are used to personalize offers: products based on past purchases appear in recommendations on marketplaces.
For sales and CRM.
AI tools take over some of the manager's work, resulting in more transactions and increased customer loyalty:
- Sending personalized emails;
- Communicate with customers: answer questions, process returns, make changes to orders;
- Segmenting customers;
- Create commercial proposals.
The M.Video-Eldorado store has created a digital assistant that communicates with the customer in real time. The consultant understands queries, gives information about the equipment and helps with the purchase.
For production.
At enterprises, AI agents are engaged in quality control, monitoring equipment around the clock, and predicting breakdowns. This helps to manage inventories, save on equipment and reduce errors:
- Analyze equipment operation to identify anomalies;
- Reject products prior to use;
- Food temperature control is monitored;
- Control the consumption of electricity.
Russian mining and metallurgical company Nornickel has introduced the following technologies: digital vision to find defective equipment and virtual sensors that determine the composition of chemical compounds.
How to create a simple AI agent: basic steps
There are enough AI services on the market to solve business tasks. But with a self-created AI agent, it is possible to customize work to the specifics of companies; to control the storage and processing of personal information; to refine and train the agent as companies develop; and to integrate the neural network into corporate systems without restrictions.
Let's break down how to create an AI agent for personal tasks in four steps.
- Determine the agent's purpose.
Answer the question: what's an agent supposed to do?
- Answering customer questions;
- Create commercial proposals;
- Analyze statistics.
- Select a platform.
There are two types of platforms: frameworks and no-code/low-code.
Frameworks - is a set of ready-made templates and tools for creating artificial intelligence. With their help, developers write code for a future AI agent. Such platforms include AutoGen and LangChain.
No-code/low-code constructors - They are used to create products without writing code or with minimal use. The programs use a graphical interface or off-the-shelf solutions. Such constructors include Tilda, Langflow and Microsoft Copilot Studio.
- Educate and provide data.
If the agent is not given relevant information, it will not solve the problem. Provide:
- Databases, instructions, FAQ;
- Manner of speaking and tone of voice;
- Client Data;
- Financial Statements.
It is important to delineate the work of the AI tool: what the agent does itself and what it will redirect to a human.
- Test and run.
Ensure that the agent is performing the tasks correctly:
- Does he answer the questions correctly;
- Whether it works correctly with the external system;
- Is it handling the load.
An agent, like a live employee, is important to train, provide tools and information to work with, and monitor how he or she is doing. As the company develops, develop the system.
Best services for working with an AI agent
Consider framework and no-code/low-code services.
AutoGen
A framework from Microsoft designed to create AI agents and the interaction between them.
To use, you need: an Azure account with an active subscription, Git, and Python 3.10 or later.
Opportunities:
- Creating networks of agents where everyone works independently or collaboratively;
- Code creation and debugging by agents;
- Interaction with external systems, API;
- Customizing the agent for a specific request.
It'll do: Developers, programmers, companies aiming at automation.
LangChain
A combo of framework and low-code created by American programmer Harrison Chase. It helps to create applications using large LLM language models.
You'll need Python 3.9+, API keys to LLM, promt templates, and memory mechanisms to save the dialog.
Opportunities:
- Developing chatbots;
- Change data sources without rebuilding the application;
- Maintaining multiple LLMs;
- Providing ready-made blocks for the agent to assemble.
It'll do: Developers and companies looking to create a personalized agent.
Microsoft Copilot Studio
Graphical low-code tool for creating agents and agent flows. Agent Flow - automate repetitive tasks and implement third-party services and systems.
To create, describe the agent, provide specific instructions, topic triggers, source of information, and desired actions.
Opportunities:
- Visual Agent Creation;
- Automating tasks;
- Assistant Training and Improvement;
It'll do: Organizations wishing to automate business processes, marketers, newcomers.
Langflow
Visual low-code tool for creating and deploying Python-based agents. It works by dragging and dropping elements together in one interface (drag-and-drop).
To create AI agents - install the service in Python and run the command 'pip install langflow'. The platform will start and a control panel will appear, with which you can create workflows.
Opportunities:
- Testing individual elements of the agent;
- Setup without programming;
- Work with PDF, TXT, DOCX documents.
It'll do: To marketers, startup, business and freelancers.
LlamaIndex
A Python data framework for building LLM-based applications. It works on the Retrieval-Augmented Generation (RAG) principle: it responds to a query using external information sources.
To work, you need to select LLM and get the API key, Python 3.9+, LlamaIndex library.
Opportunities:
- Interoperability with other platforms (LangChain, AutoGen, Langflow);
- Create indexes for data structuring and fast search;
- Shows what sources are used.
It'll do: businesses with a large database, freelancers, scientists.
The main thing about AI agents
AI agents are autonomous tools that interact with the external environment to perform tasks. They are focused on the final result and independently select options for solving the problem. A programmer and a novice can create such an assistant using frameworks (AutoGen, LangChain) or no-code/low-code platforms (Copilot Studio, Langflow). Unlike AI assistants, agents analyze the situation, select a solution and perform an action without human intervention.