AI agents that do real work inside your systems

A workflow follows steps you define, with a model handling some of them. An agent decides which steps to take towards a goal, using the tools and permissions it has. We start with the simplest design that does the job - often a workflow - and add autonomy where it pays off.

When the work splits into steps that need different tools, context or models - research, extraction, validation - and one agent becomes unreliable holding all of it. A coordinator routes the work and checks the results. Otherwise one agent with well-designed tools is simpler to run.

Yes, when an API or another approved integration path exists. We define which identity the agent uses, what it can read and change, and which actions need approval, then prove the integration in the first phase.

We agree an evaluation set before development and measure task completion, how often people had to step in, and cost per completed task. On the governed data assistant we built with Barton Malow, that evaluation showed better answers than the native NL2SQL engine on 62% of real business questions.

We work with Anthropic (Claude), OpenAI, Amazon Bedrock, Google Vertex AI and xAI, and choose per task based on answer quality, latency, cost and where your data may be processed.

For a focused automation, our 14-day AI workflow takes one use case and one data source to production for a fixed fee of €2,000–€5,000. Larger systems start with a scoping call.