AI Agent - What Is It and How Do Autonomous AI Agents Work?

What is an AI agent?

An AI agent is a system based on artificial intelligence that does not just answer questions, but independently plans a sequence of actions, uses external tools (APIs, databases, a browser), and makes decisions to complete a multi-step task without constant human supervision. It makes decisions based on data and an objective, rather than a rigid script.

This is easiest to see through a comparison. A classic model generates a single response to a single prompt and stops there. An AI agent operates in a loop: it analyzes the context, plans the next step, executes it, evaluates the outcome, and adjusts further actions. This is why it is well-suited for automating complex business processes.

How does an AI agent work?

In an iterative cycle, an AI agent observes the situation, moves on to planning the next step, takes action, checks the result, and then refines the plan. This pattern (observe-reason-act, or the reasoning loop) allows it to pursue an objective and make decisions without asking for permission at every stage. If something goes wrong, the AI agent returns to the reasoning stage instead of blindly pushing forward with the original scenario.

The planning phase involves breaking down the goal into smaller tasks and selecting the tools needed to execute them. To interact with the outside world, an AI agent uses tools (tool use): it calls APIs, queries databases and search engines, and saves data in a CRM. Thanks to this, it works with up to date information rather than relying solely on training data.

It maintains context through memory - short-term within a single session, and long-term across sessions (long-term memory), typically based on vector databases and RAG architecture. This memory enables the agent to "remember" user preferences across conversations or continue a multi-week project.

AI agent vs. chatbot

A chatbot generates a single response to a single prompt and finishes its job. An AI agent goes through a multi-stage process: it plans, invokes tools, checks results, and adjusts subsequent steps.

A chatbot acts reactively, within the limits of its knowledge from the conversation or training data. An AI agent acts proactively and connects to external data and systems. A chatbot typically remembers only the current session; an AI agent leverages long-term memory and returns to earlier decisions and user preferences without needing them restated. That is precisely how an AI agent works: it handles an issue through to completion, whereas a chatbot at that level merely responds.

AI agent vs. traditional, deterministic software

Traditional software is deterministic: for the same inputs, it always produces the same output, following rules manually hardcoded during development. It does not evaluate situations or choose between alternatives.

An AI agent is flexible because it relies on two artificial intelligence techniques. Machine learning allows it to infer patterns from data, enabling it to handle situations no one explicitly programmed. Natural language processing (NLP) allows it to understand instructions and documents in human language. When an application's layout or an API's data format changes, the AI agent identifies the change and selects a different approach - where a deterministic script would typically just fail. This flexibility comes at a cost: the agent's behavior is harder to predict, making guardrails all the more important.

Business applications of AI agents

The primary applications of AI agents are customer service, finance, and marketing, though implementations also extend to logistics, manufacturing, healthcare, education, and creative work. They share one common trait: multi-step processes that previously required human intervention at every stage. AI agents automate repetitive tasks and operate 24/7 without breaks or slowdowns, accelerating operational workflows.

The scale of adoption in business is growing rapidly. In a 2025 PwC survey, 79% of executives reported that their company was already using AI agents, and two-thirds of those implementing them cited measurable productivity gains [1]. The global AI agent market is valued at around $7.8 billion in 2025, with projections exceeding $50 billion by 2030 [2].

#1 In customer service, an AI agent handles cases end to end: it understands user questions, verifies data across systems, processes routine requests, and escalates complex issues to human agents. In e-commerce, it manages orders, tracks delivery status, and responds to customer inquiries in real time, increasing engagement. It can also analyze a customer's emotional cues in real time - tone, word choice, and sometimes vocal pitch - and adapt its response accordingly. However, this is signal analysis, not genuine emotional comprehension: AI agents still struggle with sarcasm or nuanced context, which is why humans remain involved in sensitive cases.

#2 In finance, AI agents detect fraud and block suspicious transactions - learning the baseline patterns of normal payment network activity and holding high-risk payments pending verification.

#3 In marketing (AI agent marketing), AI agents handle campaign optimization, select target audiences, and shift budgets between channels based on performance. They also generate content and support creative workflows by producing copy and ideas. They relieve copywriters of repetitive workloads: handling tasks that once fell solely to copywriters - though final selection and editing remain with humans. Prospec ting and research are a specific case of the same logic: a single task triggers dozens of subsequent operations.

#4 In logistics and manufacturing, AI agents plan routes, forecast demand, prioritize order fulfillment, and respond to supply chain disruptions by combining data from multiple systems.‍

#5 In healthcare, they organize records and compile test results - as decision support for staff, not a replacement; the AI Act treats many such applications as high-risk. In education, they personalize learning paths to match a student's preferences and skill level.

However, declared adoption outpaces real maturity: according to McKinsey (2025), only about 23% of organizations scale agents in at least one business area [3]. In our view, a solid implementation begins with a clearly defined goal and guardrails, not with the model itself.

‍

How to implement an AI agent in a company?

Implementing an AI agent starts with the process, not the technology. First, you select a single repeatable process where tasks consume the most time and slow down the team, and then you define the workflow: where the AI agent makes decisions on its own, and where human approval is required. The choice of technology and specific solution depends on this process, not the other way around.

A typical implementation path looks like this:

‍

The sequence is not accidental: first the process and guardrails, then the model. The same technology that gives the agent autonomy requires discipline - the reverse path usually ends in an impressive demo that cannot withstand production. That is why it is better to treat out-of-the-box solutions as a starting point, not as a complete strategy.

Autonomy and supervision: guardrails

An agent's autonomy is configurable, not binary. The broader the scope of actions an AI agent takes on its own, the more guardrails need to be anticipated. The very same AI agent makes decisions autonomously when doing market research, but requires approval for an irreversible operation - a wire transfer, data deletion, or publishing content.

Guardrails serve this exact purpose - technical and procedural constraints that define what the agent does on its own and what requires human approval. The most critical element is human-in-the-loop checkpoints for high-risk actions. In addition, this includes: restricting permissions to the bare minimum, validating what enters and leaves the agent, setting cost limits and attempt caps, as well as full decision logging for audit purposes.

A practical example: an agent whose only role is to analyze data in a CRM gets a read-only token, while a separate token with write permissions is activated only after operator approval. A checkpoint also triggers when the model's confidence in its own decision drops below a defined threshold.

‍

AI Agent vs. GDPR and the AI Act

An AI agent making decisions without continuous human supervision must comply with the requirements of GDPR and the EU AI Act.

The AI Act assigns every system to one of four risk levels, and the level determines the scope of obligations [4]:

• Unacceptable risk - prohibited applications in the EU, e.g., social scoring of citizens.
• High risk - recruitment, credit scoring, critical infrastructure, among others; permitted, but subject to strict requirements.
• Limited risk - transparency obligation: you must inform users that they are interacting with an AI. This obligation (Article 50 of the AI Act) applies starting August 2, 2026, and covers customer-facing conversational agents, among others [5].
• Minimal risk - no additional obligations; covers the vast majority of applications.

An agent that makes autonomous decisions in a high-risk area is then subject to system documentation, testing, decision logging, and a mechanism that allows a human to halt or reverse an action before it has irreversible consequences. However, these obligations have been postponed: the adopted Digital Omnibus amendment (Regulation 2026/1744) pushes standalone high-risk systems under Annex III to December 2, 2027, and systems embedded in products under Annex I to August 2, 2028 [6]. The classification itself and the regulations have not changed - only the application dates have shifted (as of August 2026).

Under GDPR, the most critical provision is Article 22, which restricts decisions based solely on automated processing that produce legal effects or similarly significantly affect an individual, including profiling [7].

‍