Building In-House AI Agents to Debug Machine-Written Code

The rise of agentic coding tools is fundamentally changing how development teams operate. Companies can now ship code at unprecedented velocity, but that acceleration creates a significant problem downstream: when systems fail, understanding what went wrong becomes exponentially harder if no human was directly involved in writing the code.

The Human Bottleneck in AI-Driven Development#

Site reliability engineers are caught in an awkward position. The pace of code production has accelerated, but their capacity to debug and understand that code hasn't kept pace. When something breaks, determining the root cause, crafting a fix, and preventing future failures requires deep knowledge of how systems work — knowledge that's increasingly difficult to maintain when AI agents are the primary authors.

This creates a dangerous cycle: overwhelmed humans can burn out trying to keep up with agent-generated code, while the systems themselves become less transparent and harder to troubleshoot. Meanwhile, downtime remains as costly and unacceptable as ever.

Fighting AI With AI#

The counterintuitive solution gaining traction: deploy AI agents specifically designed to perform root-cause analysis and system troubleshooting. This approach mirrors the very problem it addresses, but emerging evidence suggests it works. As machine learning models have improved, their ability to analyze failures and identify underlying issues has advanced significantly — though the OpenRCA benchmark indicates there's still substantial room for improvement.

Building Before Buying#

Sam Farid and Nate Heinrich from Chronosphere (a Palo Alto Networks company) advocate for a pragmatic path forward: organizations should attempt to build their own AI SRE agents before adopting vendor solutions.

The logic is compelling. The act of constructing an in-house agent forces teams to systematically map how their systems work, document architecture decisions, and compile organizational knowledge into structured formats that both humans and machines can reference. This documentation itself — often structured as Markdown files containing system context — becomes invaluable context that AI agents can draw on when diagnosing problems.

Paradoxically, the process of building the agent matters as much as the final product. Companies gain clarity about their infrastructure, dependencies, and operational patterns in ways that vendor tools cannot replicate.