Knowledge · agent

Automating Tasks with AI Agents: What Is Realistic Today

Which tasks AI agents can automate today: recurring processes, deadline detection, summaries – and where humans decide.

AI agents today primarily automate tasks that are recurring but require understanding: reading and classifying documents, detecting deadlines, creating summaries, completing routines on a schedule, preparing drafts. In doing so, they close the gap between classic automation, which only follows rigid rules, and work that until now necessarily depended on humans.

The difference from classic automation

Classic automation works according to "if X, then Y" – reliably, as long as the world looks exactly as it did during setup. An AI agent, by contrast, can understand content: it recognizes that "payable by 07/15" and "payment term: 14 days from invoice date" mean the same thing, even though no rule anticipated this. This makes tasks with leeway automatable – exactly the ones at which if-then systems fail.

What agents automate well today

• Evaluating documents: Reading, classifying incoming mail, invoices, and contracts, and detecting clearly documented deadlines – a letter becomes a prepared deadline instead of a forgotten one.
• Routines on a schedule: Reminders, regular evaluations, recurring tasks – once set up, they run without a chat.
• Condensing and summarizing: Getting long threads, conversation notes, or documents to the point, for example to prepare for an appointment.
• Preparing tasks: Deriving what needs to be done from processes and documents, and creating tasks with a description and sources – for your review.
• Creating drafts: Preparing replies, texts, and evaluations that a human only reviews instead of writing.

You can find a broader collection with practical cases under AI agents: 7 examples.

Where humans decide

Not everything an agent could do should it do alone. A proven principle: external impact goes through approvals. Sending emails, making calls, changing master data – the agent prepares this, you get a query and decide. There are two reasons for this: mistakes with external impact are expensive, and responsibility cannot be delegated to software. Good systems make this boundary technically visible rather than leaving it to chance.

What this looks like in practice

In webRichtung agent, the automation toolkit is called Automations – in two forms: Functions are capabilities that your assistant is allowed to carry out independently; you activate them individually and adjust settings such as the minimum confidence – if the agent isn't confident enough, it doesn't create anything but gets in touch. Schedules are fixed, recurring tasks that you set up directly in the chat. Whatever awaits your decision is collected in the Queries area.

The right way to start

Start with a task that meets three criteria: it occurs regularly, good results are clearly recognizable, and it has no external impact. Checking deadlines on processed documents is a typical first candidate – the benefit shows quickly, and errors remain internally correctable. When the results hold up over a few weeks, you expand step by step – with the same ground rules: clear specifications, graduated autonomy, approvals for anything that goes out.

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