Context Engineering in Practice: Automating the Plugin SDLC at Kestra

Kestra's development team has integrated Context Engineering into its workflow to streamline the process of building new plugin features and fixing bugs by utilizing AI agents for mechanical tasks while reserving human judgment for critical decisions. This approach, which was presented at DevLille 2026, involves using structured, machine-readable GitHub issues as complete business and technical specifications, which AI agents can parse to handle tasks such as writing code, conducting QA, and managing pull requests. The team operates at an L4a level of agentic AI adoption, where agents handle dynamic workflows with human approval checkpoints, and aims to move towards more autonomous and self-optimizing levels. The workflow is designed to maximize efficiency, reduce costs, and maintain high-quality outputs by encoding explicit domain knowledge into Skills and agents, which are controlled and updated from a central repository. This innovative method has been refined over months of real-world application, emphasizing the evolving role of software engineers from code writers to context curators, ensuring that AI tools enhance rather than replace human expertise.