Instead of forcing full autonomy, the AI agent allows teams to start with human approvals at key stages. This 'human-in-the-loop' model builds trust and enables organizations to incrementally automate complex support workflows as they grow more confident in the system's reliability.
The AI's power stems from creating a holistic knowledge graph. It integrates deep codebase analysis—including regressions and fixes—with contextual data from project management and support tools like Jira and Zendesk. This mimics how a top-tier human engineer synthesizes disparate information to solve problems.
The AI agent is designed to act like a human team member within existing systems. It performs bi-directional updates in tools like Jira or Linear—adding comments, changing statuses, and assigning tickets. This seamless integration ensures human teams maintain visibility and that established processes aren't disrupted.
The AI agent's purpose is framed not as a replacement for engineers but as a tool to augment them. Its primary function is to handle the tedious, time-consuming tasks known as 'toil'—initial triage, data gathering, and running basic tests—freeing up senior engineers for high-judgment work that requires human expertise.