AI agents: what they are and how businesses can create and use AI agents
An AI agent is a language model-based programme that does not simply answer questions, but independently performs multi-step tasks: it collects data, makes decisions, calls external services and completes the task without human intervention at every step. The difference from a standard chatbot is fundamental: a bot reacts, an agent acts.
When a business needs an AI agent, not just a chatbot
Confusion between a chatbot and an AI agent can prove costly for a business. Companies either overpay for simple automation or underestimate the capabilities and solve too few tasks. Here are situations where an agent is specifically needed:
• If the task consists of several steps, each requiring a decision. For example: receive a request → check availability in the database → calculate the cost → send a quote → set a follow-up reminder. A bot won’t do this, but an agent will.
• You need to work with external systems. An agent can access CRM, ERP, databases, and third-party service APIs, send emails, and create documents, all within a single task.
• The process takes time and requires monitoring. An agent can work in the background; you don’t need to constantly monitor the process.
• There is a large volume of similar tasks, but each requires contextual judgement. Categorising enquiries, initial processing of incoming emails, generating reports.
• Coordination of several specialised agents is required. One analyses the incoming email, another checks the data in the CRM, and a third drafts the reply. This is already a multi-agent system, and it tackles tasks that are beyond the capabilities of a single agent.
A simple test: if the task can be described as a decision tree with a finite number of branches, a bot will suffice. If the solution requires gathering information from several sources, assessing the situation and choosing from a range of options, you need an agent.
How an AI agent works
Understanding the architecture helps you realistically assess what an agent can and cannot do, and avoid falling into the trap of unrealistic expectations.
At the heart of any AI agent lies a large language model (LLM): GPT-4o, Claude 3.5, Gemini, Mistral or their open-source equivalents. The model is responsible for understanding the task, planning steps and formulating a response. On its own, it has no access to the internet, your data or external systems; it is simply a ‘thinking’ component.
The agent gains capabilities through tools. Searching a database, sending an email, making an API request, creating a document, running a script – these are all tools. The model decides: which tool to invoke, with what parameters, and what to do with the result. The quality and range of tools determine what the agent is actually capable of doing.
An agent may have several types of memory: short-term (the current dialogue or task), long-term (a knowledge base about clients, previous interactions, company documents) and episodic (a history of completed tasks for learning from experience). Configuring memory correctly is one of the key technical challenges when building an agent.
Modern agents operate in a cycle: receive a task → consider what to do → invoke a tool → evaluate the result → decide what to do next → repeat until completion. This pattern is called ReAct (Reasoning + Acting). It allows the agent to adapt to unexpected results. If the tool returns an error or the data differs from what was expected, the agent revises its plan.
For complex tasks, a single agent cannot cope or will work slowly and unreliably. The solution here is a system of several specialised agents: an orchestrator agent breaks down the task and distributes subtasks, specialised agents execute them, and the orchestrator collects the result. This is similar to how a department works: the manager sets tasks, and specialists complete them.
Common mistakes when creating AI agents
Giving the agent too broad a scope of authority at the outset
It is tempting to give the agent access to all systems and allow it to act independently. In practice, this leads to errors that are difficult to reverse. The correct approach is to start with a ‘suggest an action, human confirms’ mode, gradually increasing autonomy as you become convinced of the agent’s reliability.
Failing to plan for error handling
The agent called a tool and received an error. What next? If this isn’t accounted for, the agent will either freeze or do something unpredictable. Every tool must have a defined behaviour in the event of a failure: retry, skip, escalate to a human, or log the error. Without this, the agent is unreliable.
Confusing the states ‘agent responded’ with ‘task resolved’
An agent may generate a convincing response that is, in fact, incorrect. And for business processes, this is critical. For tasks where an error is costly (prices, legal documents, financial data), verification is required, either via a separate verification agent or through mandatory human confirmation.
Failing to log the agent’s actions
If the agent has done something wrong, you must be able to understand exactly what, at which step, and why. Without detailed logs, this is impossible. Logging all tool calls, input data, and agent decisions is an essential part of any business system.
Practical tips for creating an AI agent
Start with one specific process, rather than ‘automating everything’.
Choose a single repetitive process with a clearly measurable outcome. Processing incoming requests, generating reports, initial lead qualification. Something specific where the impact of changes is clearly visible. This will yield quick results and an understanding of how agents work in practice.
Describe the process step by step before starting development.
Draw a diagram: what goes in, what steps are involved, what decisions are made at each step, what comes out, and what constitutes an error. The more precisely the process is described, the easier it is to build the agent and the fewer surprises there will be during operation.
Plan for human oversight of the system from the very beginning.
Determine in advance: at which steps the agent acts independently, and at which human confirmation is required. This is not a limitation; it is an architectural decision that makes the system reliable. As data on the agent’s performance accumulates, you can gradually reduce the number of control points.
Choose tools for the task, not for the trend.
LangChain, AutoGen, CrewAI, n8n with AI nodes, custom development – each approach has its own strengths and limitations. Off-the-shelf frameworks speed up the start-up, but can limit flexibility. Custom development offers control, but takes more time. The choice depends on the complexity of the task and integration requirements.
Allow 30–40% of the time for testing with real data.
An agent that performs excellently on test cases often behaves unpredictably on real data containing typos, non-standard formats, and edge cases. Testing on real data before going live with clients is essential.
Frequently asked questions
How does an AI agent differ from a standard chatbot?
A chatbot operates according to pre-defined scripts: question → answer. An AI agent plans its actions independently, invokes tools, works with external systems and adapts to non-standard situations.
Which language model should I use?
It depends on the task. GPT-4o is a well-balanced choice for most business tasks. Claude 3.5 Sonnet performs better when working with long documents and following complex instructions. Gemini is effective to use if integration with Google Workspace is important. Open-source models (Llama, Mistral) – if data cannot be transferred to the cloud. For a pilot project, we recommend starting with GPT-4o or Claude, as they deliver predictable results.
Is it safe to give an agent access to corporate data?
This is a question of architecture, not the nature of the technology. It is possible to build an agent that works only with data within the company’s perimeter, sends nothing to the cloud, and logs every action. The opposite is also possible. Before implementation, it is necessary to clearly define: what data the agent can see, what it can do, and how its actions are logged.
Conclusion
AI agents are not the next generation of chatbots. They are a different class of tools: they perform tasks, rather than answering questions. For businesses, this means the ability to automate processes that previously required human judgement at every step. The technology only works when it is backed by a clearly defined process, a well-thought-out architecture and realistic expectations. An agent launched without an understanding of what it is and why the business needs it will lead to disappointment. An agent designed for a specific task delivers measurable results.
Author:
Anton Kucher, Managing Partner at Meta-Sistem
Experience: over 10 years in website and web system development
Specialisation: website and web application development, integration and business process automation
Author profile:
LinkedIn: https://www.linkedin.com/in/anton-cucer/
Meta-Sistem: https://meta-sistem.md