February 13, 2026
Kyle Getty
Read Time: ~6 minutes
<aside> 📌 At Tilt, we seek individuals who challenge personal assumptions, value ownership and trust, and strive for excellence to inspire and empower their team. If this article connected with you, join our team!
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<aside> 📎 On-call shouldn’t start with archaeology at 2am. This post shows how to build an AI-powered incident response system that kicks off the moment an alert fires, pulls the runbook, queries the logs, reviews the latest commits and feature-flag changes, then drops a Slack-ready root-cause teaser with evidence attached. The trick is a clean hybrid: AI for reasoning and synthesis, APIs for facts, all stitched together with structured, typed outputs so the whole workflow is autonomous.
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https://open.spotify.com/episode/307GpYlj99dyNjEMxoo00v?si=6Dq7-ld-RP-3XngqgQ2ZjQ
Modern DevOps teams face a critical challenge: when production incidents occur, engineers need to quickly analyze logs, review recent code changes, and identify root causes, often in the middle of the night. What if AI could handle the initial investigation automatically?
This post explores three key patterns for integrating AI into DevOps workflows, using a real-world incident response system as an example.
The most powerful way to use AI isn’t just getting text responses, it’s getting typed, structured data that your system can act upon. By treating AI as a strongly-typed object model, you can ensure consistent, parseable results.
OpenAI for example provides structured model outputs with Agent Client Protocol we can build a C# based model to interact with the Augment CLI Auggie
This allows us to leverage the benefits of the Augment Context Engine when doing things like code analysis. While also abstracting how we interact with an agent so we can focus on the needed orchestration.
Here’s how to transform free-form AI agent responses into structured objects this uses Auggie with ACP. Using System.Text.Json schema exporter we can easily fold in the expected output into our prompt.
public async Task<T> RunAsync<T>(string instruction, CancellationToken cancellationToken = default)
{
var typedInstruction = BuildTypedInstruction(instruction, typeof(T));
var response = await RunAsync(typedInstruction, cancellationToken);
return ParseTypedResponse<T>(response);
}
public static string BuildTypedInstruction(string instruction, Type responseType)
{
var sampleFormat = GetSampleJsonFormat(responseType);
return $"""
{instruction}
IMPORTANT: Provide your response in this EXACT format:
<augment-agent-message>
[Optional: Your explanation or reasoning]
</augment-agent-message>
<augment-agent-result>
{sampleFormat}
</augment-agent-result>
The content inside tags must be valid JSON that matches this structure:
{GetTypeDescription(responseType)}
""";
}