No More "Digging" Through Logs: Designing AI as a Pre-processor for Human Decision-Making

System alerts never stop. In daily operations, engineers were expected to open Splunk, search for the relevant logs, read through long error messages and stack traces, and then determine the impact and whether any action was required. This process imposed a high cognitive load and created a psychological barrier for the team. As a result, initial responses tended to depend on specific individuals, triage was sometimes delayed, and our regular alert review meetings became longer and increasingly exhausting.

In this session, we will share a practical case study of AI-driven operations that helped us reduce “alert fatigue” and shorten a daily alert review meeting from more than 30 minutes to just 5–10 minutes—less than one third of the original time. By integrating our internal FaaS platform, Funk, with the Splunk API and generative AI through ChatAI Proxy, we built an architecture that allows the entire workflow—from log collection to root-cause analysis and decision support—to be completed directly in Slack.

The session will cover:

• A system architecture for event-driven automated log retrieval using an internal FaaS platform and the Splunk API
• A UX shift from “humans search for and read logs” to “AI translates logs and delivers diagnostic insights”
• Techniques for improving AI diagnosis accuracy by incorporating past incident-handling examples into prompts
• A design approach that combines rule-based automation, such as automatic reactions, with LLM-based reasoning to reduce the burden of human decision-making
• Practical points for building an operations flow that stays within Slack instead of forcing engineers to move across multiple tools

This is not just a story about trying out generative AI. It is a hands-on example of how to embed AI into an existing operations workflow and dramatically reduce the cognitive load on engineers.

Attendees who struggle with daily alert handling or operational maintenance, as well as those who want to integrate generative AI into real business workflows and achieve tangible results, will gain reusable design patterns and operational know-how that can be applied immediately in their own teams.