AI Agents, Automation & Chatbots
What is an AI agent?
Definition: what makes something an AI agent
An AI agent combines a language model as its “reasoning core” with the ability to actually act. The model handles understanding the task and planning; through connected tools it can also make things happen – query a database, draft an email, check an invoice or run a web search. The key difference from a plain chatbot is that an agent does not just answer: it derives and carries out steps by itself.
- Goal instead of single command: the agent receives a goal and largely finds the path itself.
- Tool use (function calling): access to systems, APIs, files or search.
- Memory: intermediate results and context are kept across several steps.
- Level of autonomy: from semi-autonomous (a human confirms each step) to largely independent.
How an AI agent works: the action loop
At its core, an AI agent runs a repeating loop: perceive, reason, act, check – and start over if needed. This loop lets it tackle tasks that cannot be fully planned in advance, because the agent decides anew after each step. That is both its strength (adaptability) and its challenge (less predictability).
- Understand & plan: the LLM breaks the goal into sub-steps.
- Act: it calls a suitable tool, such as a database query or a calculation.
- Observe: the tool's result flows back into the context.
- Evaluate & continue: goal reached? If not, the next step follows.
AI agent vs. classic automation
Classic automation – workflow rules, scripts or RPA (robotic process automation) – follows a fixed “if-then” flow. It is fast, cheap and highly predictable, but breaks as soon as a case deviates from the expected pattern. An AI agent is the opposite: it copes with unstructured inputs and exceptions, but is less deterministic and needs closer oversight. In practice the best solution is often a combination: fixed automation for clear, rule-based parts, and an agent for the steps that require language understanding and judgement.
- Automation: deterministic, rule-based, ideal for high volumes and clear processes.
- AI agent: flexible, language-capable, good with ambiguity – but costlier and less predictable.
- Rule of thumb: the clearer the rules, the more you lean to classic automation; the more judgement is needed, the more an agent fits.
Practical examples for SMEs
The following examples are illustrative and show typical usage patterns in a Swiss SME – not measured results. In each case the point is to connect the agent closely to existing systems and to require approvals for sensitive steps.
- Customer service: an agent reads a request, looks it up in the order system, drafts a reply – an employee approves it.
- Quotes & offers: from an email enquiry, price list, availability and a template are combined into a draft.
- Research & summarising: an agent searches internal documents (RAG), summarises them and cites the sources.
- Scheduling and back-office tasks: reconciling calendars, creating records, pre-sorting receipts – with control over bookings.
Limits, risks and guardrails
AI agents are powerful but not infallible. They can invent facts (hallucination), fall for manipulated inputs (prompt injection) or take actions no one intended. That is why productive use needs guardrails: tightly scoped permissions, confirmation for consequential steps, logging and a human who can step in. The larger an action's potential impact, the tighter the control should be.
- Data protection: for personal data the revised Swiss FADP (revDSG/nFADP) applies, overseen by the FDPIC; clarify where data is processed and stored.
- EU AI Act: thanks to its extraterritorial reach it can also affect Swiss firms serving the EU.
- Human-in-the-loop: never fully automate approvals for payments, sending or deletions.
- Traceability: log actions, keep permissions minimal, spot-check results.
Frequently asked questions
What is the difference between an AI agent and a chatbot?
A chatbot mainly responds with text. An AI agent can also act: it uses tools, calls systems and carries out multi-step tasks to reach a goal. Many chatbots only become agents once you give them tools and room to act.
Are AI agents the same as RPA or automation?
No. RPA and classic automation follow fixed rules and are highly predictable. An AI agent decides situationally and copes with ambiguity, but is less deterministic. Often the combination is strongest: rules for clear flows, an agent for the steps that need language understanding and judgement.
How autonomous should an AI agent be?
As autonomous as necessary, as controlled as possible. For low-risk, easily reversible tasks more autonomy can make sense. For consequential steps – payments, sending, deletions – a human should approve. Rule of thumb: the larger the potential impact, the tighter the control.
What risks do AI agents bring?
The main ones are hallucinations (fabricated content), prompt injection (manipulation via smuggled-in instructions), unintended actions and data-protection questions. Countermeasures are minimal permissions, approvals for sensitive steps, logging, testing and clarifying where personal data is processed under the Swiss FADP (revDSG/nFADP).
Does my SME need its own models or programming for this?
Usually not. Many agents can be built on existing language models and connected to your systems via standard interfaces. What helps is a narrow use case, clean data integration and clear guardrails – rather than building your own models from scratch. That keeps effort manageable and control high.
Key terms in the glossary
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