What this is about
Agentic coding isn't a tool trend — it's a new model for engineering work.
When AI agents don't just suggest but fully handle engineering tasks, it's not just speed that changes — the entire way engineering works changes.
A classic engineering team has a product owner, developers, QA, and DevOps. In a fully agent-based Scrum team, AI agents take on these roles in a structured and documented way — guided by clearly defined tasks, review checkpoints, and governance rules. What matters isn't whether AI is better than humans, but whether the setup ensures the quality, traceability, and steerability that enterprise contexts demand.
How an agentic Scrum team works
A virtual Scrum team works reliably only when roles, responsibilities, and feedback loops are clearly defined. That means: defined agent roles (product owner, developer, reviewer, QA), clear task boundaries, structured commits, and a steerable governance layer. Where review and approval checkpoints are properly embedded, such a team can be integrated into existing product and delivery processes.
- defined agent roles following Scrum logic
- clear task boundaries and measurable outputs
- structured code reviews and approval steps
- steerable governance and full traceability
- integration into existing delivery structures
Tools in use
Agentic coding at digitario is not a demo topic — it is daily practice in real project work. Claude Code is used for larger contexts, bigger codebases, and structured code changes — especially when architectural understanding and careful, traceable work are required.
OpenClaw is an open-source agent framework by Peter Steinberger that runs locally and connects to multiple AI models. It never forgets and acts autonomously around the clock — powerful, but not yet consistently production-ready. Experienced guidance is essential for a clean rollout.
These form the basis for agentic workflows: structured sequences of agent tasks with defined inputs, outputs, and review checkpoints — repeatable, documentable, and embeddable in delivery processes.
Practice, not theory
digitario builds and runs agentic setups actively — not as a showcase, but as a working mode. Evaluating such a setup for an enterprise context takes more than tool knowledge — it requires an understanding of team dynamics, governance, delivery integration, and real limits.
That is the basis for a sound assessment: what works, what does not, and how an agentic team can be set up in a specific context.
Typical starting points
An agentic setup is most effective where tasks are clearly defined, outputs are measurable, and review structures are in place. Typical contexts include clear, repeatable engineering tasks, a high demand for speed and capacity without immediately growing headcount, or environments where cloud APIs are not an option for sensitive engineering work and local setups are needed.
What makes an agentic setup reliable is not the model alone, but how roles, tasks, and feedback loops are structured. Agent roles must be clearly defined, outputs must be traceable and reviewable, and governance and review must be embedded into the delivery logic.