AI agents can do more than answer questions. They can take on defined tasks, combine information from different systems, prepare cases and trigger processes. This gives businesses and public administrations the opportunity to reduce repetitive work — without giving up control, data protection or professional responsibility.

Leapfrog develops and operates AI agents that fit your processes, systems and security requirements. Depending on the use case, the agents can work in an assistive, semi-supervised or fully autonomous way.

From assistance to execution

Many work processes consist of recurring steps: searching for information, checking data, summarising content, preparing documents, classifying requests or updating systems. AI agents can take on these tasks and support employees where manual routine work currently takes up time.

Typical areas of use include:

• pre-sorting customer and citizen requests

• searching internal knowledge bases

• creating draft responses and documents

• preparing tickets, cases or applications

• reconciling data between systems

• making specialist processes accessible via chat or web interfaces

Not every agent needs to act fully autonomously. In many cases, a semi-supervised approach makes sense: the agent carries out preparatory work, identifies open issues and involves people whenever a decision, approval or assessment is required.

Securely connected to existing systems

The value of AI agents emerges when they are connected to the right data and tools. We develop connectors for existing specialist applications, databases, document management systems, CRM, ERP and ticketing systems, as well as internal APIs and file repositories.

Through controlled interfaces, agents can access approved information and perform defined actions. This means they do not work with arbitrary knowledge, but with the current and relevant data of your organisation.

Architecture approaches such as MCP servers play an important role here. They create a structured connection between AI models, tools and data sources — including permissions, context management and security rules.

Guardrails: clear boundaries for automated action

AI agents are meant to reduce workload, not act without control. That is why we develop agents with clear guardrails.

These include role and permission concepts, allowed and prohibited actions, approval steps, logging and escalation rules. Critical processes can be automatically handed over to employees. Sensitive actions can be limited to specific user groups, data sources or process steps.

This creates automation with traceable control: the agent knows what it is allowed to do, which information it can use and when a human needs to be involved.

Data protection and sovereign operations

Especially when administrative, customer or company data is involved, data protection is a central part of the architecture. We ensure that data flows, model access, storage and permissions match the protection requirements of your organisation.

Depending on your requirements, we operate AI agents in a private cloud, on your own infrastructure or in suitable data centre environments. We take GDPR requirements, data minimisation, access control and logging into account.

The goal is a solution that is not only powerful, but also aligned with your requirements for data protection, compliance and digital sovereignty.

Monitoring, operations and continuous improvement

AI agents are not a one-off demo, but productive software components. That is why we consider development and operations together.

We set up monitoring, logging and quality controls so that agent runs remain traceable. Errors, escalations and usage patterns can be evaluated. This allows agents to be improved step by step and adapted to new processes, data sources or requirements.

Our services

Leapfrog supports you from the first idea to productive operation:

• analysis of suitable use cases

• prototyping and technical feasibility checks

• development of agents, connectors and interfaces

• integration into existing systems and specialist applications

• implementation of guardrails, roles and approval processes

• privacy-compliant architecture and sovereign operations

• monitoring, support and continuous optimisation

Start step by step, scale securely

Getting started with AI agents does not have to be large or risky. Often, a clearly defined use case is the best starting point: an internal assistant, a semi-automated process or an agent for research and document preparation.

A prototype can then grow into a productive solution — securely integrated, monitored and aligned with the requirements of your organisation.