AI Developer Productivity Sprint

A fixed-price project from €9,900 in which I implement a concrete use case with coding agents in your real codebase. The result is a working agentic harness with the context, rules and tools of your project, documented findings and a recommendation for rollout, guardrails and tooling.

A coding agent for individual developers is quick to set up. The sprint targets the step after that: AI in long-grown codebases, across several teams and dependencies, with maintainable and architecturally consistent results. That includes specifications, context building, quality assurance and reviews, not just the tool.

Usually not because of the tool but because of the surroundings. Typical causes are missing context on architecture and conventions, vaguely worded tasks, missing tests as feedback for the agent and a review that becomes the bottleneck. On top of that, everyone in the team works differently. The sprint addresses exactly these points, using a concrete use case in your codebase.

Without guardrails it does: the agent delivers plausible code that experienced developers have to check laboriously. With project rules, architecture context and automated checks, the agent catches many errors itself before a person looks at it. Review effort is therefore one of the sprint's acceptance criteria.

That depends on your contracts, your data and the choice of tool. Technically, the options range from cloud providers with processing in the EU to self-hosted models where the code never leaves your network. The sprint shows which option fits your requirements; the legal assessment stays with your data protection officer and lawyers. A clear rule matters, because otherwise developers use AI tools without agreement.

With one concrete use case instead of a broad rollout: a task with recurring effort, a verifiable result and manageable risk. This produces a solid result in your real codebase, and the decision about the next step rests on experience rather than expectations.

With a common basis that applies to everyone in the team instead of many individual solutions. That way, good experiences of individuals become usable for the whole team, and results become comparable. What this looks like for your team and how it can be transferred to further teams is described in the recommendation at the end of the sprint.

The environment in which a coding agent works reliably: project rules, architecture context, build and test commands, access to the right tools, and checks that catch faulty output. Without a harness, an agent produces plausible but non-integrable code. The sprint delivers this harness for your repository.

By acceptance criteria we define together in the kick-off: which tasks the agent should complete on its own at the end, in what quality and with what review effort. The evidence is merged results in your real repository, not a demo project, plus your team's assessment.

That depends on your requirements for data protection and operation, not on a preferred vendor. The sprint checks which tools and models fit your team and your constraints, from cloud services with processing in the EU to self-operated solutions. You receive a reasoned recommendation that you can also defend internally. Your code stays in your environment; the work happens in your repository.

A project limited in time and price that tries AI in software development on one concrete use case with coding agents in your real codebase, instead of introducing it broadly. The AI Developer Productivity Sprint is such an AI sprint: fixed price from €9,900, and at the end a working agentic harness, an evaluation against agreed criteria and a recommendation on whether and how a rollout is worthwhile.

The agentic harness for your repository, the documented assessment against the agreed criteria and a recommendation that explains why not to roll out. That is the basis for avoiding a costly wrong decision on licences and onboarding a whole team.