Jina Debug: AI on call engineer that resolves production issues

We were frustrated by the reactive nature of incident response. Engineers were constantly getting pulled into firefighting mode, spending hours digging through logs and tracing issues back through codebases during high-pressure incidents. We knew there had to be a better way to handle on-call rotations and incident management. The core problem was the massive time drain and context-switching cost of incident response. When alerts fire at 2 AM or during critical product launches, engineers need to quickly understand what went wrong, why it happened, and how to fix it. This typically involves: - Parsing through thousands of log lines - Correlating errors across different services - Understanding the relevant code context - Determining root cause and appropriate fixes This process was manual, time-consuming, and heavily dependent on tribal knowledge. We actually started by building a QA testing agent. During that work, we noticed that the same capabilities we were developing such as understanding codebases, analyzing logs, identifying failure patterns were exactly what on-call engineers needed during incident response. The key insight was that incident investigation follows similar patterns to automated testing: you need to understand what the system should do, analyze what it's actually doing, identify the gap, and propose solutions. Once we recognized this parallel, we also decided to address the on-call engineering use case, which had even more immediate pain and value for development teams.