What Are AI Agents and Why Are They Actually Needed?

Search engines and simple algorithms cannot solve challenging problems today. Programmers used to write code for every case; now, however, an AI agent can handle all types of complex tasks — analysing data, making decisions, executing actions — without needing constant human supervision. Automation has been brought to a whole new level, improving productivity for companies by giving them the tools to operate more efficiently.

"In my 8 years with automation, I've been through a variety of approaches — 43 in total — ranging from traditional bots to multi-agent systems. My primary conclusion is that AI agents allow people to accomplish tasks that previously required entire teams. For instance, one of our agents managed a market flash crash overnight and generated tens of thousands of dollars for clients while they were sleeping."

According to companies that have adopted AI agents, their productivity has increased by up to 35–40%. These numbers confirm the importance of AI agents for optimising business operations.

What Is an AI Agent?

An AI agent is an artificial intelligence-based software module that collects input through various means, interprets this data in relation to a defined objective, and takes action towards achieving that objective. Unlike traditional software, AI agents can learn from past experiences, adapt to changing circumstances, and constantly evolve.

Key characteristics of AI agents:

• Autonomy: They act independently of ongoing human involvement — they are responsible for identifying a sequence of actions required to reach their objectives.
• Goal-oriented behaviour: AI agents have an objective they strive towards rather than simply executing instructions.
• Reactivity: They respond quickly when something changes in the world around them — a new message, a sudden price movement, or a new order.
• Social ability: They can communicate with other agents, APIs, and human users.

Key differences between an AI agent and a traditional program:

In short, a traditional program is like a shopping list that says "Buy Milk" — whereas an AI agent can find the nearest store, check prices, evaluate your preferences, and recommend the best option.

Why AI Agents Are Valuable

AI agents make it easier to automate tasks that previously required significant manual effort. They free up professional resources from repetitive data collection, continuous tracking of key variables, news aggregation, and report generation.

Take the cryptocurrency market, where timing is everything. AI agents can respond instantaneously to blockchain signals, assess associated risks, and execute trades faster than any human could.

Many companies pay thousands of dollars per month for subscriptions to analytical services like Glassnode, Messari, and Dune Analytics. The major shortcoming is that the underlying data is fragmented and must be processed manually — which is prone to human error. An AI agent can compile, analyse, and deliver a structured answer in under 10 seconds, starting from $29/month, while eliminating emotional mistakes and saving both money and stress.

Practical examples of where this is useful:

• Trading: The agent continuously monitors large traders' positions on Ethereum, tracks sentiment in Telegram, and generates trading signals — even while the trader is asleep.
• Customer support: The agent identifies the intent and sentiment of a customer inquiry and routes it, along with a drafted response, to the appropriate department.
• Financial analytics: The agent produces a daily expense report, detects anomalies, and alerts the management team before the start of the working day.

A real-world example: on the night of October 11th–12th, 2024, the crypto market experienced a well-known flash crash — Bitcoin dropped 8% and most altcoins fell 15–40%. The vast majority of traders ended up in the red. However, clients using ASCN.AI's spot-futures arbitrage agents made anywhere from several thousand to hundreds of thousands of dollars by capitalising on spread differences between funding rates and exchange rates. The agents operated entirely autonomously — without panic, without emotion, purely on logic.

How AI Agents Work

Building Blocks of an AI Agent's Architecture

A modern AI agent consists of four main modules working in synergy:

1. Base Model (Foundation Model)

This is the "brain" of the agent — a large language model (GPT-4, Claude, or a specialised LLM trained on crypto and DeFi data). It understands requests, generates text, and provides reasoning and decision-making capabilities. Models differ based on their training data; those trained on private, domain-specific data tend to produce more accurate and relevant outputs.

2. Memory Module and Context

All interaction history, requests, results, and errors are stored here, which means the agent maintains context and avoids repeating past actions. If you asked about BTC yesterday and ask about ETH today, the agent recognises the connection and provides a consistent response.

3. Tool Integration

This module gives the agent access to external APIs, blockchain nodes, databases, and messenger applications. In ASCN.AI, agents have access to Ethereum and Solana nodes, exchange data feeds, Telegram and Twitter parsers, and news aggregators. Think of each tool as a different "hand" the agent uses to interact with the world.

4. The Perceive → Think → Act Cycle

• Perceiving: The agent receives data — a Telegram message, a price change, or a new order.
• Thinking: The agent processes the received data using its underlying model and uses memory to determine what should be done next.
• Acting: The agent takes the appropriate action — sending a message, saving data, or generating a report.

This cycle continues indefinitely. After each action, the agent evaluates the outcome and adjusts its course as needed.

Autonomy and Goal-Oriented Behaviour

Autonomy means the agent independently determines how to achieve its goals without waiting for commands at each step. For example, asked to collect news about token X over the past week, the agent identifies its own sources, searches, filters, and prepares the summary. If one source is unavailable, it automatically switches to another.

Goal-oriented behaviour means the agent stays focused on achieving a specific result. If one method doesn't work, it finds an alternative.

Learning, Adaptability and Reflection

• Learning: The agent accumulates knowledge through historical data embedded in its model and through real-time experience stored in memory.
• Adaptability: The agent responds quickly to market changes and expands its toolkit without requiring full retraining.
• Reflection: The agent analyses both successes and mistakes to improve its logic, incorporating user feedback along the way.

Types of AI Agents and Their Functions

Virtual Assistants and Chatbots

These agents communicate with users through text or voice, answer questions, and automate support workflows. ASCN.AI's assistant specialises in cryptocurrencies; banks use similar assistants for account management.

Core functions: natural language understanding, response generation, request routing, and personalisation. Common applications include customer support (up to 70% of inquiries resolved without human involvement), consultations, and feedback collection.

Autonomous Systems and Robots

Any device or software that operates independently. Examples include self-driving cars, delivery drones, and automated trading robots.

Core functions: navigation, real-time decision-making, and physical execution. Applications span warehouse logistics, industrial automation, transportation, and algorithmic trading.

Decision-Making Agents

These agents process data and provide recommendations without performing physical actions. For example, ASCN.AI uses an agent to retrieve on-chain data, news, whale activity, and market metrics — and produce a risk and opportunity report in 10 seconds.

Multi-Agent Systems

Groups of specialised agents working collectively on complex tasks, exchanging information and coordinating their actions. A typical trading example: one agent monitors the market, a second analyses sentiment, a third generates signals, and a fourth executes trades — together forming a complete, automated strategy.

How to Work with AI Agents

Interaction Interfaces

• Text interface (chatbot): Ask a question in a messenger or web interface and receive a natural language response.
• Voice interface: Ask by voice and receive either an audio or text response.
• API and integrations: Connect the agent to CRM, ERP, and analytics systems for business-level automation.
• No-code platforms: Build automations from pre-built blocks without any programming — for example, ASCN.AI NoCode.

Steps to Implement an AI Agent

1. Define the task — what the agent needs to do.
2. Select the right tool — a pre-built assistant or a no-code builder.
3. Connect data sources — CRM, APIs, blockchain nodes, messengers.
4. Configure the logic — trigger rules, actions, and how results are delivered.
5. Test in test mode before going to production.
6. Launch, monitor performance, and adjust as needed.

Best Practices

• Start small — perfect one simple task before expanding.
• Use feedback to continuously improve the agent's performance.
• Don't replace humans where creativity and empathy are required.
• Use multiple agents for different tasks.
• Monitor API costs and optimise query logic.

Underlying Technologies

Machine Learning and Neural Networks

AI agents are built on large language models (LLMs) trained on vast amounts of text data. The neural network predicts the next word in a sequence based on the current context. Universal models like ChatGPT are trained on open datasets and may be limited in narrow domains. Specialised agents like ASCN.AI are trained on private crypto market data, which produces more accurate and relevant outputs.

Natural Language Processing (NLP)

NLP enables agents to understand human speech — including spelling variations, typos, slang, and contextual meaning. Key components include:

• Tokenization: Breaking text into words and phrases.
• Lemmatization: Reducing words to their base form.
• Named Entity Recognition (NER): Identifying specific entities within text.
• Sentiment Analysis: Determining the emotional tone of text.
• Intent Classification: Understanding what the user wants from the agent.

Through attention mechanisms and contextualisation, models focus on what matters in a query and filter out noise. Models must account for the full context of a conversation within a context window of 4,000 to 128,000 tokens. When a dialogue exceeds that limit, earlier messages may be lost — which is addressed through the use of external memory systems.

Technical and Ethical Challenges

Data Privacy and Security

Key risks include data leaks during storage and transmission, unencrypted API keys, and prompt injection attacks that can force an agent to carry out unintended actions.

Recommended safeguards: use encrypted keys, anonymise all data processed by the agent, and restrict access to sensitive information.

In ASCN.AI, all requests are processed anonymously, and no message history is retained on servers after a conversation ends.

Ethics and Responsibility

As AI agents become more widely used, the question of who is responsible for an agent's erroneous decisions is increasingly important — and current legislation hasn't caught up yet. Key guidelines to follow:

• Disclaimers: The agent should make clear it provides analysis, not financial advice.
• Transparency: Explain how the agent arrived at its conclusions.
• Access restrictions: Actions with significant consequences (e.g., withdrawing funds) should require human confirmation.

Developers also bear responsibility for preventing misuse — spam, manipulation, and deepfake creation.

Technical Limitations

• Running large models is expensive and costs continue to rise.
• Sequential API requests introduce latency.
• Rate limits restrict the number of requests within a given time period.

These issues can be mitigated through caching, batching, and selecting the right model for each task.

The Future of AI Agents

Current Trends

• Multimodality: Integrating text, audio, and video for deeper understanding.
• Autonomous entrepreneurial agents: AI that can build websites, run ads, and manage revenue.
• Blockchain integration: Crypto wallets, on-chain transactions, smart contracts.
• Federated learning: Training on distributed data without moving sensitive information.

Promising Areas of Application

• IoT — smart homes and industrial automation.
• Education — AI agents as personal tutors.
• Healthcare — diagnostics and patient monitoring.
• Creative industries — music, design, and script generation.

Impact on Society and the Labour Market

Automation of routine roles alongside the creation of new positions: AI auditors, AI ethicists, AI agent developers. Broader access to professional-grade analytics. Faster decision-making, driving increased competition across industries. And an open question: the risks of data monopolisation and AI concentration.

FAQ

What is an AI agent in simple terms?

A smart assistant that can make decisions and perform tasks independently, adapting to the situation — unlike a traditionally programmed application.

What kinds of tasks can AI agents handle?

Automation of routine processes: data analysis, customer support, report generation, market monitoring, and request processing.

Can I build an AI agent on my own?

Yes. Using no-code platforms like ASCN.AI NoCode, you can build a basic agent without any coding knowledge. For more complex projects, you'll need Python and frameworks such as LangChain or AutoGPT.

How much will an AI agent help me?

AI agents free up time from routine work so you can focus on strategy and creativity. They can significantly increase efficiency and reduce costs.

What are the limitations of AI agents?

Inaccuracies due to insufficient data, lack of empathy and common sense in edge cases, computational costs, and risks associated with improper use.

Practical Recommendations

How to select the right AI agent for your business

1. Clearly define the tasks you want the agent to handle.
2. Estimate the volume and type of data required.
3. Verify compatibility and integration with your existing systems.
4. Test the agent using real data.
5. Compare the cost of the agent against the cost of manual labour.

Common mistakes and how to avoid them

• Don't try to automate everything at once — pick one process and start there.
• Train employees on how to work with AI agents.
• Always verify key decisions made by AI agents before acting on them.
• Monitor expenses and optimise query logic.
• Use feedback from employees to identify and fix errors.

Effective use and scaling

• Use templates and reuse successful processes wherever possible.
• Document all settings and operating logic for future reference.
• Combine multiple agents to cover different tasks.
• Monitor performance metrics and adjust settings as needed.
• Scale gradually — don't try to roll everything out at once.