Custom AI agent development for B2B software
AI agents are more than chatbots: they make decisions, call tools, process data and run multi-step workflows on their own. We develop custom AI agents and multi-agent systems for SMBs and startups — and embed them into your existing software.
Autonomous AI agents for B2B: reliable, not just plausible
An autonomous AI agent in a B2B setting has to do more than answer plausibly: it has to find the right record, create a ticket, call an API, verify the result and know when a human should decide. That reliability is exactly what we build — with clear boundaries on what the agent is allowed to do.
We use established AI agent frameworks where they help, and lean custom code where frameworks get in the way. What matters is not the framework but a software architecture in which agents stay testable, observable and replaceable.
Agentic workflow architecture and multi-agent systems
We break complex tasks into roles: one agent plans, others research, verify or execute. Such multi-agent workflows need deliberate architecture — otherwise you end up with systems nobody understands. Our agentic workflow architecture follows a few simple rules:
- Every agent has one clearly scoped task and a defined set of tools
- Deterministic steps stay classic code — the model only decides where judgment is needed
- State, hand-offs and results are logged and traceable
- Human-in-the-loop wherever mistakes would be expensive
- Evaluation on real cases before an agent goes to production
AI agent automation: typical use cases
Where does AI agent automation pay off? Wherever people currently gather information from several systems, check it and pass it on: support triage, document and contract processing, order handling, internal research or preparing decisions. The agent takes the routine, your team takes the exceptions.
Agentic AI with data protection and operations in mind
Agents access data and systems — so permissions, data protection under GDPR and the Swiss FADP, and the choice of model provider are part of the design from the start. We build AI agents that can be hosted in the EU or Switzerland when the use case requires it, and ship monitoring so you can see what the agent does and what it costs in production.
Frequently asked questions about AI agents
What is the difference between a chatbot and an AI agent?
A chatbot answers. An AI agent acts: it calls tools and APIs, works through several steps, verifies results and hands over to humans when needed. That is how it takes on real work in a process.
When do we need a multi-agent system?
Only when a task consists of clearly separable subtasks that need different tools or perspectives. Many use cases are solved by a single, well-built agent — we recommend the simplest architecture that works.
Which AI agent frameworks do you use?
We know the common frameworks and use them where they save time. Just as often we build lean custom structures because they are easier to test and operate. The choice depends on your stack and your team.
How do you make sure an agent does not go rogue?
Tight permissions, deterministic steps in code rather than in the model, evaluation on real cases before go-live, human-in-the-loop at critical points, and monitoring in production.
Which task should an agent take over?
Tell us about your process — we show you whether and how an AI agent can handle it reliably.