Agents and automation that actually take work off your plate

Reliability does not come from the model name, it comes from the design. We build agents with clear boundaries: they only get access to the tools and data they need, they operate within a defined task, and they have explicit instructions on what to do when uncertain. For production we test every agent on a set of realistic scenarios, including edge cases and adversarial use. We measure success rate, hallucination rate and escalation rate. You get a dashboard showing weekly what the agent did, what went well and what did not. Only when those numbers are stable do we scale volume. Small and correct first, then scale.

We think about that upfront, not after the fact. Every agent has a fail-safe: when confidence drops below a threshold, it escalates to a human via Slack, Teams or email. For real errors (wrong answers, hallucinations, failed API calls) the incident is logged with full context: input, prompt, model output, steps taken. So you can review and adjust. We also build in a correction loop by default: users can give feedback and that feedback improves prompts and retrieval over time. An agent that never makes mistakes does not exist, an agent that makes mistakes visible and learns from them does.

Human-in-the-loop is the rule for us, not the exception. For every agent we explicitly define when a human must step in: low confidence, sensitive decisions (finance, legal, complaints), unknown input patterns, or simply when a customer asks. Escalation goes through the channel your team already uses: Slack, Teams, a ticketing system or email. The team member receives full context, the agent's proposal, and can approve, adjust or take over with a single click. You decide how strict the thresholds are. A new agent runs stricter than an agent that has proven itself.

For truly sensitive data we build on-premise or in an EU-only cloud environment that you control. Nothing leaves your infrastructure. For less sensitive use cases we work with providers (Anthropic, OpenAI, Google) that contractually guarantee inputs are not stored or used for training. We document per use case where the data goes, how long it is retained and who has access. For clients with strict GDPR requirements or sector-specific rules (healthcare, legal, financial) on-premise is often the best route. Vector databases like PGVector or Weaviate can run locally, as can open models like Llama or Mistral. You have the choice.

In most cases yes. We work daily with CRMs (HubSpot, Salesforce, Pipedrive, Teamleader), accounting systems (Exact, Twinfield, Yuki), email (Outlook, Gmail), document platforms (SharePoint, Drive, Dropbox), telephony (RingCentral, Twilio, Aircall) and chat platforms (WhatsApp Business, Intercom). If a system has an API we integrate directly. If it does not, we work through Zapier, Make, n8n or as a last resort browser automation. We always start with a short technical check so we know upfront whether an integration can be robust, or whether a workaround is needed. No surprises mid-project.

The AI Act imposes requirements on logging, transparency and human supervision, especially for agents that affect people (customers, employees, applicants). We therefore build audit trails by default: every agent decision is recorded with input, output, model version, timestamp and any human approval. For agents that may fall into a high-risk category (e.g. recruitment, credit scoring or medical advice) we set up logging even stricter: the prompt version and retrieval sources are also retained. We also ensure transparency to end users: they know they are talking to an agent and how to escalate to a human. You get a compliance file per agent.

By picking a use case that is well-defined and where the pain actually sits. Not "we want AI agents", but "our reception gets 200 booking requests per week and that costs an hour a day". Such a use case we can often have operational in one or two weeks, with a limited pilot group. Then we measure what it delivers in time or quality, adjust, and expand. An AI Quickscan upfront helps choose the right use case, see /en/ai-strategie. We no longer build six-month platform projects. Having something in production that works, small but real, is far more valuable than a large roadmap without a first delivery.