Fighting AI with AI — Lawrence Jones, Incident
Summary
Incident.io, a company specializing in incident response management, has developed an "AI SRE product" to automate production investigations and manage the complexity of its AI systems. These systems, which analyze hundreds of telemetry queries, logs, metrics, traces, and historical incident data, are too complex for human debugging. The company uses AI unit tests, called evals, defined in YAML files, to verify prompt changes. To address the challenge of managing large, unmaintainable eval files and the inability of coding agents to process them due to context limits, Incident.io created a CLI tool, "eval tool," enabling agents to interact with eval suites. Furthermore, to debug multi-prompt AI systems, they convert UI debug data into downloadable file systems, allowing tools like Claude Code to analyze system behavior and identify modification points. For scalable analysis across thousands of customer accounts, Incident.io employs AI analysis pipelines, using parallel sub-agents and structured markdown playbooks in a tool called "scrapbook" to generate detailed reports on system performance and suggest improvements.
Key takeaway
For AI Architects and Machine Learning Engineers building complex AI systems, prioritize developing internal debugging tools that seamlessly integrate with coding agents. Converting UI-based debug information into downloadable file systems significantly enhances agent context, allowing for more effective root cause analysis. Additionally, consider implementing AI-driven analysis pipelines with structured runbooks to scalably evaluate system performance across diverse customer environments, saving substantial time in identifying and resolving issues.
Key insights
AI systems managing other AI systems require specialized tools for debugging, evaluation, and scalable analysis.
Principles
- Evals function as AI unit tests for prompt validation.
- File systems are effective context for AI agents.
- AI runbooks enable repeatable, reliable complex analysis.
Method
Translate UI debug data into downloadable file systems for agent analysis. Implement AI analysis pipelines with parallel sub-agents and structured markdown playbooks for scalable, repeatable system performance evaluation.
In practice
- Use a CLI tool to enable agents to manage eval test cases.
- Convert complex UI debug views into text-based file systems.
- Develop AI runbooks for automated, structured analysis of system performance.
Topics
- AI SRE
- Incident Response Management
- Eval Datasets
- Coding Agents
- File System Debugging
Best for: AI Architect, Machine Learning Engineer, AI Engineer, MLOps Engineer, Director of AI/ML
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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Engineer.