AI Engineering Best Practices Guide – Building High-performance Software with AI

AI coding agents are transforming how software is designed, developed and delivered, but unlocking their potential requires more than choosing the right model. This practitioner’s playbook offers engineers and technical leaders a practical framework for building reliable, scalable and secure software with AI agents.

The third instalment in GreySpark’s four-part series, the report explores a fundamental principle: reliable outcomes depend on the engineering harness surrounding the model, not the model alone. Just as importantly, the quality of the specification directly shapes the quality of the resulting code.

The playbook takes readers through the complete agent-assisted development lifecycle, from assessing organisational readiness and creating effective specifications to planning, implementation, review and evaluation. It examines how teams can use parallel agent workflows to scale delivery while maintaining consistency, visibility and control.

Beyond development practices, the report addresses how organisations can measure the performance and impact of AI coding agents across teams. It also considers the security, governance, oversight and accountability requirements that become increasingly important when agent-authored code is deployed in regulated environments.

Whether your organisation is experimenting with AI coding agents or scaling their use across engineering teams, this playbook provides actionable guidance for turning AI-assisted development into a disciplined, measurable and sustainable engineering capability.