From Data to Decisions and Actions

Government agencies, insurance providers, and organizations receive a large number of applications every day. Each application must be reviewed, classified, and evaluated against applicable legal requirements.

This work is highly repetitive, yet at the same time critical. Incorrect assessments, overlooked exclusion criteria, or missing information lead to delays, additional inquiries, and dissatisfaction.

The LLM agent therefore performs a structured pre-assessment. It identifies the requested benefit, retrieves the relevant legal foundations from the database, and systematically evaluates the applicant’s information against these requirements.

The result is not an automated decision, but a well-founded and traceable recommendation, including justification and references to relevant legal provisions.

Without access to approved internal sources, AI-generated answers remain risky. RAG reduces hallucinations, improves traceability, and makes knowledge accountable.

A technician asks a seemingly simple question: 'Can we postpone maintenance of this system by one month?'

A generic AI would provide a broad answer based on assumptions. A RAG system, however, incorporates approved maintenance manuals, service bulletins, and internal operational documentation.

The system identifies that a specific installed component has a documented increased failure risk if maintenance intervals are exceeded. In addition, the system is operating in a production environment.

The resulting answer is therefore not 'probably safe,' but:

RAG does not generate plausible answers—it delivers context-based and accountable decisions.

Automation without approval can damage trust. The agent creates the draft, but delivery only takes place after human review.

A company is planning an exclusive customer event. Customer information, agenda details, and text templates are already available. Fully automated delivery would be technically possible—but communicatively risky.

The LLM agent therefore first creates a personalized invitation message. It considers the recipient’s role, relationship, and context, and highlights sensitive aspects such as tone and timing.

Only after explicit approval by marketing or sales is the invitation sent. The result is controlled, high-quality communication instead of uncontrolled automation.

An agent can collect information, identify risks, and evaluate options. Responsibility remains where it belongs: with the human decision-maker.

A customer asks support: 'Is the latest firmware suitable for my storage system?' What sounds simple is, in reality, technically complex.

The LLM agent does not provide a generic answer. Instead, it restructures the request, identifies missing information, and retrieves relevant content from approved knowledge sources such as release notes and upgrade guides.

After evaluating the available information, the agent determines that the new firmware is compatible but requires a prior hard drive firmware update. A direct upgrade would involve significant risk.

Instead of providing a guess, the agent delivers a justified recommendation, including the correct sequence of steps, risk mitigation guidance, and maintenance recommendations.

The final decision remains with the human—but it is based on complete context, validated rules, and documented expertise.

Traditional automation triggers actions based on predefined rules. Agents additionally evaluate context, dependencies, and risks before acting or requesting approval.

An inventory level falls below a defined minimum threshold. A traditional automation system would immediately trigger a replenishment order.

An LLM agent, however, evaluates the broader context: outstanding deliveries, production schedules, supplier status, and historical consumption patterns.

Instead of placing an order automatically, the agent prepares a recommendation, suggests alternatives, and presents the decision for controlled approval.

Agentic processes reduce costs without sacrificing control.