AI agents: opportunities, limitations and real-life examples

Imagine it's 02:57 in the morning...

Your online forum moderation team is fast asleep - but your AI agent is wide awake and blocks the latest scam from a spammer. What happens when artificial intelligence doesn't just reply, but takes action itself? In the next ten minutes, you will find out why AI agents are becoming increasingly important - and where they like to trip over their own feet.

What are AI agents anyway?

An AI agent is an autonomous AI component that independently pursues goals, uses tools and makes decisions. Imagine an extremely motivated intern - only without coffee and rest breaks.

Technically speaking, an AI agent usually consists of:

• LLM (Large Language Model): The brain of the whole.
• Tool layer: Tools and interfaces (APIs, apps).
• Memory: Cross-step memory.
• Autonomy: Authority to take the path to the goal yourself.

Why now?

The rapid developments of LLMs - especially the ability to plan, analyze and interact with tools - combined with stable, well-documented APIs, make AI agents truly affordable and practical for the first time today.

But with technical feasibility comes a new challenge: agents are only really useful if they are not just generally "smart", but have specific domain knowledge. A support agent who speaks the customer's language but knows nothing about internal processes? Quickly frustrating. A research agent without access to specialist sources? More of a gimmick than a real benefit.

This is why the combination of powerful technology and a deep specialist context is crucial. The most successful agents are currently created where this context is systematically built in.

From theory to practice: 3 agent examples

1. migipedia SPAM-Detection - The 24/7 bodyguard

The Online community Migipedia Migros receives thousands of new user reviews of products every day. Spam and unwanted posts regularly appear, which cannot be detected reliably and quickly enough by humans alone. Our AI agent automatically recognizes these posts in real time, reliably classifies them and immediately forwards critical cases to the moderation team. This keeps the community clean and allows the moderators to concentrate on more demanding tasks.

2nd PHLU interview chatbot with support team

The Lucerne University of Teacher Education (PHLU) uses our AI agent in research to Interviews with students more efficiently. The Open source chatbot conducts semi-structured interviews independently and asks in-depth questions. In the background, the chatbot is supported by a moderator agent, which prevents it from digressing from the interview topic, and an agent that remembers the students' most important findings in order to build on them in the advanced interview. This saves researchers an enormous amount of time and at the same time increases the consistency and quality of the data collected.

3rd SMARTalks: From lunch talk to blog post

SMARTalks are informal, informative lunchtime talks that we regularly record in Swiss German. Our agents process these video recordings and automatically create high-quality blog articles from them. They master dialect variations and create easy-to-read texts that can be published shortly after the event. This makes valuable insights quickly and easily accessible.

Stumbling blocks: Where agents still fall flat on their faces

AI agents have enormous potential - but they also come with their own challenges. Here are the most common stumbling blocks you should be aware of and what you can do about them:

Hallucinations

AI agents sometimes simply make things up. This is in the nature of LLMs, which derive patterns from language and do not check facts. In sensitive areas, this can have fatal consequences.

Tips for mitigation:

• Use a second LLM to check the output of the first one (e.g. fact checker setup).
• Weise Agents are obliged to cite sources for statements.
• Allow human feedback and include a loop where users can report questionable content.

Costs & latency

AI agents can quickly become expensive and slow, especially if they run large models for every little thing or process unnecessarily large contexts.

Tips for mitigation:

• Use smaller, specialized models for routine tasks and reserve large models only for critical paths.
• Use caching strategies for frequent requests.
• Monitor and limit context lengths specifically to reduce token costs.

Evaluation

AI systems are not deterministic - the same input can lead to slightly different outputs. A simple "green tick" test is not enough here.

Tips for mitigation:

• Define clear tasks and use cases that can be objectively evaluated in order to compare models with new versions.
• Use score metrics and benchmarks instead of binary true/false tests.

Agent Drift (Death Spiral)

When AI agents perform multiple steps without intermediate checks, small errors can add up. The result? An agent that works diligently but completely misses the mark.

Tips for mitigation:

• Introduce regular intermediate checks (e.g. every 3-5 steps).
• At each step, let the agent briefly validate the original goal before continuing.
• Establish relapse strategies ("rollback") in the event of a deviation from the target.

Data protection

Especially in companies, the reflexive grab for "all data" is dangerous - not only technically, but also legally.

Tips for mitigation:

• Only feed the agent with the data that is really necessary.
• Use models that meet your data protection standards (e.g. on-prem or EU-hosted).

Tool chaos

An agent is only as stable as the APIs and tools it accesses. If a tool changes or an interface fails, the agent is left helpless in the worst case scenario.

Tips for mitigation:

• Integrate regular tool checks into your DevOps process.
• Let the agent report itself if a tool does not work as expected (self-monitoring).
• Use robust wrappers and fallback mechanisms to absorb failures.

Tips for your first AI agent

Document processes clearly before you automate
Only those who understand their existing processes can automate them effectively. Half-finished or unclear processes lead to error-prone agents.

Human-in-the-loop
People should be able to provide feedback or control decisions for all critical paths. This creates security and trust.

Better LLMs are not always the answer
A larger model alone does not provide a sustainable advantage if the overall system is weak. Invest in tools, data processing and systemic robustness.

The gap between pilot and production is bigger than you think
What looks good in the demo does not automatically scale. Plan for real user numbers, data protection, costs and system integrations.

Speed > Perfection
Don't let the desire for perfection slow you down. A functioning MVP for a sub-process with quick feedback will get you further than the perfect overall process on paper.

Understanding in the company counts twice
Rely on logs, telemetry and transparent decision-making processes to ensure quality and trust in the long term.

Don't forget the UX & wow factor
A technically good agent is useless if it is not used. Integrate it into existing workflows in a meaningful way and create aha experiences.

Be ambitious
Now is the time to dare to do something big. Technology is maturing - and so is your advantage if you experiment boldly today.

While you sleep, your agent works

Today, AI agents are more than just digital helpers. They change processes, save time and open up new possibilities. Now is the ideal time to gain initial experience.

Ready for your first AI agent prototype? We are happy to help you!