---
title: "What AI Agents Are and Why They're Changing the Rules of Software"
description: "An AI agent isn't a better chatbot — it's a system given a goal that it works out how to reach on its own. Here's how agents work, where they add real value, and what powers frameworks like openclaw and hermes."
canonical: "https://interfase.sk/en/news/what-are-ai-agents"
lang: "en"
last_updated: "2025-05-20"
---

# What AI Agents Are and Why They're Changing the Rules of Software

> An AI agent isn't a better chatbot — it's a system given a goal that it works out how to reach on its own. Here's how agents work, where they add real value, and what powers frameworks like openclaw and hermes.

Published: 2025-05-20

- An agent isn't just given a question like a chatbot — it's given a goal, which it reaches through a cycle of planning, using tools, and continuously evaluating results.
- The frameworks openclaw and hermes handle the technical foundation of agents, with the former suited to long-running processes and the latter to precise, deterministic workflows.
- Agents deliver the most value in customer support, document processing, and internal operations such as reports or system syncs.
- Without limited permissions, human oversight on sensitive steps, and thorough logging, an agent with access to systems is a risk.

Over the past year, "AI agents" has become one of the most talked-about phrases in software — and also one of the most misunderstood. Most businesses picture a better chatbot. In reality, it's a fundamentally different category of tool: a system that isn't given a question, but a **goal** — and it works out how to reach it on its own. At INTERFASE we build these solutions for real-world production use, so we've set out what an agent actually is, where it makes sense, and what to watch out for.

## A chatbot answers. An agent acts.

A classic chatbot works in a single step: it receives an input and generates an output. An agent works in a loop — it breaks the task down into steps, carries them out using tools, continuously evaluates the result, and repeats until it reaches the goal. The difference is a bit like the one between a calculator and an accountant: both can crunch numbers, but only one of them knows what needs to happen next.

| | Chatbot | AI agent |
| --- | --- | --- |
| Input | A question | A goal / task |
| Number of steps | One | As many as needed |
| Access to tools | No | **Yes** |
| Memory | Within the conversation | Long-term |
| Output | Text | A completed task |

:::callout info
**In short:** if you expect an answer from the system, a chatbot will do. If you expect it to actually do something for you, you need an agent.
:::

## Anatomy of an agent

A language model (LLM) on its own is just a text generator. It becomes an agent once you wrap it in four layers:

- **Planning.** The model breaks the task down into a sequence of steps and decides the order. The clearer the task and the context, the more reliable the plan.
- **Tools.** Through defined interfaces, the agent calls APIs, reads from databases, runs code, and sends emails. Tools are what give it real reach beyond text.
- **Memory.** Short-term memory holds the context of the current task, while long-term memory (a knowledge base, history) lets it pick up where it left off and learn from the past.
- **The loop.** The *think → act → check the result → continue* cycle runs until the goal is met or it hits a defined boundary.

This loop is exactly what distinguishes an agent from a one-off answer — and it's also what needs the closest watching.

## The frameworks powering agents

Building an agent "from scratch" means solving a lot of tedious but critical plumbing: reliable tool calling, memory management, retries on failure, safety boundaries. Specialised frameworks are increasingly taking over this layer. Two we consider worth watching:

- **openclaw** — an orchestration framework for long-running agents. It focuses on reliable tool calling and robust long-term memory, so the agent retains context across many steps and tasks. It suits cases where the agent isn't producing a single answer but accompanying a longer process.
- **hermes** — emphasises structured, predictable output and deterministic workflows. It's the right fit where you need the agent to stick to an exact format and sequence of steps rather than improvise — in integrations and processes where "creativity" is more of a risk than an asset.

Choosing a framework isn't a matter of dogma. What decides it is the nature of the task: a long autonomous process versus a precise, repeatable workflow.

:::callout warning
**Watch out:** a framework won't fix a poorly defined brief. Without precisely defined tools, clean data, and clear boundaries, even the best agent is just an expensive text generator with access to your systems — which is a risk, not an asset.
:::

## Where agents deliver real value

They have the biggest impact where decision-making over data repeats and where people currently lose time switching between systems:

- **Customer support.** The agent looks up an order, checks its status in the internal system, and replies with real context — not a generic stock phrase.
- **Document processing.** It extracts structured data from invoices, contracts, or emails and writes it into the right system, including checks and flagging unclear cases.
- **Internal operations.** Routine tasks across your software — reports, syncs, alerts — run without a person having to trigger them.

Based on what we've seen, a well-deployed agent can take over a substantial share of the routine steps in a given process (estimated time saved, %):

:::chart bar % time saved
Support,45
Documents,60
Reporting,55
Onboarding,35
:::

These numbers aren't a promise — they're the order of magnitude we see in projects. The real benefit always depends on the specific process, data quality, and the scope of automation.

## It doesn't work without guardrails

An agent with access to your systems is a powerful tool and a potential risk. That's why we approach it with the same discipline as any production software:

- **Scope of permissions.** The agent is allowed to do only what it strictly needs — nothing more.
- **Human-in-the-loop.** A human makes the call on sensitive or irreversible steps.
- **Logging.** Every decision and tool call is recorded and traceable.
- **Gradual rollout.** We start narrow and expand scope only once the agent reliably handles the smaller slice.

> A good agent isn't one that can do everything. It's one that reliably does a single thing well — and one you can trust to do it.

## Conclusion

AI agents are shifting software from a tool you *use* to an assistant that *acts* on your behalf. Frameworks like openclaw and hermes handle the technical foundation, but success ultimately comes down to three things: quality data, precisely defined tools, and clear boundaries. If you're weighing up where an agent would make sense in your business — and where it wouldn't — [get in touch with us](/kontakt). We're happy to walk through it with you in detail.


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Web version: https://interfase.sk/en/news/what-are-ai-agents
llms.txt: https://interfase.sk/llms.txt