Agentic automation in practice: putting standard engineering work on autopilot
Aug 28, 2026Nikhil Fadnis

Summary
This post details the implementation of an agentic automation system for security vulnerabilities. It introduces Bitbucket Agentic Pipelines, Rovo Dev, and Atlassian MCP tools to create a three-part system: a dispatcher that finds and classifies eligible work items, a coding agent that applies fixes using codebase-specific skills, and a closer that verifies deployments and transitions tickets. Key technical contributions include defining agents as versioned pipeline steps, using .rovodev prompts and skills to encode institutional knowledge, and implementing idempotent workflows for reliable re-runs. The system achieved a 95% first-run merge rate for automated vulnerability fixes.
More from the AI-Native Software Development Capabilities in Jira trail
- A simpler way to run AI agents in Bitbucket Pipelines
- We’re bringing governed agent loops to the AI-Native SDLC
- How we made vulnerability fixes review-ready with Agentic Pipelines
- Opening the door to agent autonomy: The architecture behind Rovo’s agent harness
- From prototype to production: lessons learned taking AI-built software to enterprise scale