Atlassian launches DX features for AI-native engineering

Per an Atlassian blog post from the DX team, Atlassian released four major features intended to improve visibility and operational control as engineering teams adopt AI assistants. The announced features are an AI chat interface for interacting with organisation data, AI Code Insights (a set of reports exposing AI-generated code and usage patterns), proactive SLA-based alerts, and an Agent Experience metric that scores agent effectiveness across teams.

Per the blog post, AI Code Insights comprises three main reports. The AI Code Overview report buckets SDLC metrics by the percentage of AI in each PR, AI-generated code attribution shows which commits and PRs contain AI-generated code, and Agent Experience insights surface agent-session signals such as missing context or ambiguous instructions. The Agent Experience capability provides an overall agent effectiveness score filterable by team. The release also includes proactive alerts tied to SLAs and a conversational interface for querying internal data, according to Atlassian.

Engineering organisations attempting to measure AI tool impact commonly lack direct telemetry linking AI outputs to delivery outcomes. Observable signals such as commit-level attribution, token spend, and agent-session diagnostics are emerging patterns vendors use to close that gap. Practitioners evaluating similar tooling typically focus on integration with CI/CD, signal fidelity, and the noise-to-action ratio for alerts.

adoption and usage metrics (AI-generated-code percentage by team), correlations between AI-generated code and code quality or review cycles, the stability of agent effectiveness scores over time, and how SLA-based alerts integrate into existing incident and SRE workflows.