Scale your work with AI agents.

A typical engagement runs in three phases. First I sit with your team and watch the real work: which tasks eat the time, which tools people already use, and where they get stuck. Then I set up the agents for that work: configuration, instructions and playbooks, and the integrations your stack or your marketing tools need. Finally I work alongside your engineers or marketers in paired sessions and reviews until the new way of working holds without me. Scope is agreed per outcome, not per hour.

It is rarely the technology. The same patterns come up again and again: agents rolled out without being set up for the team's actual work, no clear guidance on which tasks to hand over, no shared prompts or playbooks, and nothing after the first workshop. People also have to trust the output before they change how they work, and that is a question of behaviour. I studied psychology before I became an engineer, so I work on both layers, the people and the software, and find the real bottleneck before I suggest a fix.

We agree on baseline metrics before we start. For engineering teams that is typically PR cycle time, time to first commit on new tasks and the share of work done with agents; for marketing teams, time from brief to published piece and output per person. Team satisfaction counts for both. Once the new workflows are in place, we measure again. The goal is a shift you can defend to your CFO, not a feeling that things are faster. If the numbers do not move, we have a problem to solve together.

That is the point. I leave behind documented playbooks, configured agents (instruction files such as CLAUDE.md, agent configurations, prompt libraries) and at least one internal champion who can extend the setup. Success is not 'Luka is still in the building'; it is that your team keeps producing more after I have gone. Top-up training and ad-hoc consulting are there when something genuinely new comes up, but they are optional, not a built-in dependency.

Training builds skills: workshops, hands-on sessions and multi-day programmes that teach your engineering or marketing team to work with AI agents. It fits when the gap is mainly knowledge. Consulting goes further: I find out why adoption is not sticking, design the workflows and the agent setup, and embed them with your team over several weeks. Often the answer is both: set up the system first, then train the wider organisation to use it well.

Off-the-shelf tools are good at general tasks. A custom agent system matters when it has to work inside your stack, use your own data or follow complex business rules. The systems I build run multi-step workflows (syncing CRM data, running inventory checks, routing approvals) that a chatbot or a simple API wrapper cannot handle. The decision usually comes down to whether the workflow is your competitive edge or just a productivity aid.