Investigate with agents
Agents map a relevant code path, identify dependencies, and organize traces and hypotheses. Your senior engineer decides what the evidence supports.
The Mottobits AI delivery model
Put agents inside a disciplined engineering team.
Use AI across the work that slows an existing product down: understanding the code, investigating performance, writing regression checks, and implementing bounded changes.
Artifact: investigation brief
Implement a bounded change
Agents work from an approved change contract, existing repository rules, and explicit checks. The senior developer reviews implementation, tradeoffs, and maintainability.
Build an independent safety net
Agents draft test cases and fixtures. The QA engineer checks the expectations, permission boundaries, failure paths, and actual execution evidence.
Keep the technical judgment senior
Adnan guides architecture and .NET performance decisions. The team demonstrates the result, states what remains uncertain, and agrees the next slice.
Your tools. A repeatable operating model.
Make AI useful
in your actual repository.
We work with client-approved assistants and coding agents. The first slice establishes repository context, permissions, commands, test data, and review gates before expanding automation.
AI usage and licenses belong in the agreed project setup. This website and its public MCP tools do not make paid model calls.
What we bring into the work
- Scoped repository instructions and clear agent task boundaries
- Investigation, implementation, and QA review briefs
- Reusable synthetic fixtures and meaningful regression checks
- CI evidence that separates executed results from assumptions
- A team accountable for decisions and a maintainable handover
Start with a real problem
Bring AI into the backlog you already have.
Start with a performance bottleneck, a critical workflow to protect, or one modernization slice.