The article focuses on creating a Production Engineer agent using GraphRAG, aimed at improving incident response in complex enterprise systems. When a service failure occurs, engineers often scramble for context, piecing together information from scattered dashboards, Slack threads, and outdated documentation. This disjointed approach slows down resolution times, as engineers need clarity on what’s happening and who should be involved. The proposed agent will streamline this process by automatically identifying affected services, diagnosing issues based on known dependencies, and surfacing relevant historical context, thereby reducing the time between incident detection and action. The architecture of this system centers on a straightforward input-output model. Alerts from monitoring tools like Prometheus trigger webhooks to a FastAPI server, which acts as the entry point for incident data. The server forwards this information to an Agent Controller, which orchestrates the response by fetching contextual knowledge from the GraphRAG component, a Neo4j graph database that organizes information about services, teams, and dependencies. This coordinated approach ensures that the right context is delivered to the relevant teams via Slack, linking them to necessary documentation and runbooks. GraphRAG plays a key role as the agent’s structured memory, ensuring that knowledge about past incidents and system relationships is readily available. By leveraging this architecture, the agent not only enhances the clarity of incidents but also empowers engineers to act with greater confidence. The focus is on designing a system that anticipates the needs of production engineers, reducing reliance on individual memory and improving overall incident management.