Typical starting points
When this service is a good fit
- Recurring research and knowledge tasks
- Multi-step processes spanning several systems
- Manual hand-offs between tools and teams
- Assistants for internal or external users
Custom AI agent development
AI agents that work with your systems and processes.
We build AI agents that handle defined tasks, use approved tools and interact with existing data sources or APIs. Responsibilities, permissions and failure paths are deliberately constrained rather than left open-ended.
Strategic guidance before implementation
Custom solution instead of a standard tool
Secure integration into your system landscape
Operation and continuous development considered from the start
Scope
What we deliver
- Requirements and process analysis
- Agent architecture and tool integration
- Roles, permissions and security design
- Testing, evaluation, logging and production operations
Use cases
Where AI Agents can create concrete value.
The right architecture depends on the process, data, existing systems and the cost of a wrong decision. These examples are common use cases, not blanket recommendations.
01
Internal research and knowledge assistants
02
Preparation and summarisation of complex cases
03
Tool-enabled support and service processes
04
Multi-step data and document processing
05
Assistants embedded in business applications
06
Agentic sub-processes with human approval
Decision criteria
When the approach fits – and when it does not.
We first assess whether AI genuinely adds value compared with conventional software, search or workflow automation for the specific task.
Questions we clarify early
- How variable are the inputs and tasks?
- Which actions may the agent execute autonomously?
- Which errors are acceptable, and which are not?
- Where are approvals or hard rules required?
Quality and control
AI capabilities need measurable quality criteria.
Non-deterministic outputs should not be judged from a handful of impressive demos. We define representative test cases, observe failure patterns and design fallbacks or human approvals where the business context requires them.
- Representative test cases instead of demo prompts
- Least-privilege tool and permission boundaries
- Traceable execution steps
- Fallbacks and human-in-the-loop controls for critical actions
How we work
From business problem to robust solution.
We do not start with a model name. We start with the process, users, available data and technical constraints. From there, we design an architecture that balances business value, privacy, cost, maintainability and operations.
Analysis and target architecture
At the start, we clarify the current situation, users, data sources, integrations and quality requirements. If core assumptions are still open, we shape an initial phase specifically to test those assumptions.
Development and integration
The software is developed iteratively against real data and interfaces. Critical integrations and AI capabilities are tested early so technical risks become visible before the end of the project.
Operation and continuous improvement
For production systems, monitoring, cost control, logging, failure paths and model or provider changes are part of the architecture. An AI project does not end with an impressive demo.
Frequently asked questions
Key questions about AI Agents.
Direct answers to points that should be clarified before making a technical decision.
What is the difference between an AI agent and a chatbot?
A chatbot typically responds to user input. An agent can also break a goal into steps, call tools and execute defined actions. That can create more value, but it also raises the bar for permissions, testing and failure handling.
Can an AI agent work with existing software?
Yes, where suitable APIs or controlled interfaces are available. Direct actions should be protected by explicit permissions, validation and traceable logs.
When is a traditional workflow the better choice?
When a process is fully predictable and rule-based, a deterministic workflow is often simpler, cheaper and more reliable. Agents are most useful when inputs or decision steps are genuinely variable.
Can you build an AI agent for internal business processes?
Yes. Typical projects connect an agent to internal data sources, APIs or line-of-business systems. The important design questions are restricted permissions, auditable tool calls and human approval for critical actions.
Can an AI agent access existing APIs and applications?
Yes, provided suitable interfaces exist. Integration should be server-side, use minimal privileges, validate actions, log relevant events and define clear failure paths.
Related services
Relevant building blocks for your project.
Let’s turn your AI idea into a concrete software project.
In an initial conversation, we clarify the objective, current situation and sensible next steps.
To get started, your objective, existing systems and the main open question are enough.