The Business Challenge
The business situation the organisation faced before the project began.
The startup is growing fast and developers can't keep up with the increase of business requirements, which causes some value-adding features to be deprioritized.
How the Solution Was Delivered
Discovery, design, development, testing and rollout — the journey, not the tooling.
- 1Mapped the existing delivery workflow and targeted the highest-friction stages — planning, review, and QA — for automation
- 2Defined narrow responsibilities and handoff contracts for each agent role: planner, implementer, code reviewer, security reviewer, QA.
- 3Built the agent loop and orchestration harness on the Claude Agent SDK, with a virtual file system so agents could read and modify code safely.
- 4Isolated all agent-run code execution using sandboxing (gVisor / Firecracker) as a non-negotiable security boundary.
- 5Encoded review and acceptance criteria so review, security, and QA agents evaluate against explicit standards rather than open-ended judgment.
- 6Kept humans in the loop at two gates — plan approval and final merge — to preserve accountability over what ships.
- 7Rolled out incrementally on lower-risk tickets first, then widened scope as agent output proved reliable.
Business Outcomes & Impact
The measurable outcomes from the underlying AI Deployment — with the story behind them.
Improved product feature delivery by 100%.
Business Outcome Categories
The story behind the numbers
Increased the team's feature-delivery throughput and cleared previously deprioritized backlog items, while shifting engineers' time from routine implementation and review toward higher-leverage work. Consistent automated code review, security review, and QA also raised the baseline quality and security of changes before they reached human reviewers.
Lessons Learned
What surprised the team, what worked well, and what would be done differently.
- Sandboxing isn't optional — isolating agent code execution (gVisor / Firecracker) has to be designed in from the start, not bolted on.
- Narrow, well-defined agent roles produce better results than one large general-purpose agent
- Explicit review and acceptance criteria matter more than model choice — agents are only as good as the standards you encode.
- Human approval gates at planning and merge preserve accountability and catch what agents miss
- Starting on low-risk work builds trust in the system before you widen its scope.