AI Agent
An AI agent is a software system that, based on an LLM, autonomously plans and executes multi-step tasks: it uses tools such as file systems, databases or APIs, evaluates intermediate results and works iteratively towards a given goal — under human oversight.
What distinguishes an AI agent from a chatbot?
Both are based on an LLM — the difference lies in what is built around the model:
| Chatbot | AI agent | |
|---|---|---|
| Mode of operation | answers individual requests in a dialogue | pursues a goal across many steps |
| Tools | none or few (e.g., web search) | file system, databases, APIs, command execution |
| Result | text as a suggestion | executed work: changed files, created tickets, verified results |
| Duration | seconds per answer | minutes to hours per task |
| Control | human reviews every answer | human sets direction, decides escalations, approves |
A chatbot is thus a very capable autocomplete in conversation. An agent is a recipient of delegation: you hand it a well-scoped piece of work and expect a result, not just a text suggestion.
How does an AI agent work?
The basic mechanism is a loop: the agent breaks the task into steps (plan), executes the next step with a suitable tool (act), looks at the result (observe) and decides how to proceed — until the goal is reached. Decisive for reliability is the behavior at the edges: a well-built agent escalates everything that lies outside its responsibility or requires human judgement, instead of glossing over it and guessing.
Limits: why one agent should not do everything
Two systematic limits shape every LLM-based agent. First, context: the working memory is finite, and the quality of decisions tends to decline as it fills up — an agent that accumulates everything it has seen over a long task gets quietly but steadily worse. Second, rule-following: if a single agent is supposed to pay attention to architecture, security, quality and domain concerns at the same time, it reliably stops following any rule correctly beyond a certain number of rules.
The practical consequence: instead of trying to make one agent good at everything, you split the judgement across several narrow, specialized agents — each looks at the task from exactly one viewpoint, carries only the rules of that viewpoint and runs in fresh, isolated context on every invocation. An orchestration workflow fans the work out to the appropriate agents, merges their results and escalates real conflicts to the human.
Human oversight: delegating does not mean letting go
Autonomy is not an end in itself. The value of an agent is measured by whether you can trust its result without supervising every step — and this trust arises through structure, not hope: clearly scoped responsibilities, review steps during the work rather than only at the end, and deliberately few but fixed human touchpoints — setting the direction, weighing in on the rare genuine decision questions, and approving the finished result before it takes effect.
AI agents in practice: the workflow counts, not the model
Whether agents create real value in an organization depends, in our experience, less on the model used than on the workflow around it: which tasks are suitable for delegation at all, how the agents are connected to existing systems and quality gates, and whether the agent definitions exist as versioned, reviewable files that the team itself owns and evolves — instead of renting a black box. Without this structure, it stays at impressive demos; with it, the agent becomes a reliable part of daily work.