AI agents consulting
AI Agents Consulting in Switzerland
AI agents become useful when they can use tools, follow process boundaries, gather evidence, and create outputs that people can review. Fanktank helps Swiss companies identify where agentic workflows are worth building and how to keep them reliable.
Hands-on experience with multi-agent orchestration, retrieval, tool execution, structured outputs, and verification loops.
Focus on workflows where agent actions can be observed, constrained, evaluated, and improved over time.
Practical integration mindset for existing software, documents, APIs, and business processes.
Agents need boundaries
The hard part is not making a model call a tool once. The hard part is defining what the agent may do, what evidence it must collect, when a human must approve, how errors are detected, and how the system behaves when the model is uncertain.
Start with workflows, not technology
Good agent use cases already have repeated tasks, clear inputs, observable outputs, and a review path. Examples include document analysis, research workflows, internal support, code generation support, reporting, and operations tasks that require several tools.
Production agent architecture
A production agent needs state management, retrieval, tool permissions, validation, tracing, fallback behavior, and cost controls. Those parts matter more than the demo framework.
Good AI agent candidates
- A workflow requires multiple research, data, or tool steps.
- The output can be reviewed by a person or a deterministic validation check.
- The task happens often enough that automation has clear value.
- The system can start as an assistant before it becomes more autonomous.
Common Questions
Are AI agents ready for business use?
Yes, for bounded workflows with clear tools, review steps, and evaluation. Fully autonomous open-ended agents are still risky for many business contexts.
Can agents connect to our internal tools?
Often yes, but access should be scoped carefully. Tool permissions, audit logs, and human approval matter when an agent can change real systems.
What is a good first AI agent project?
A good first project has repeated steps, source evidence, reviewable output, and low blast radius if the agent needs correction.