Georgia State University's TReNDS research center has deployed an AI-powered automation system that diagnoses production errors in under 60 seconds—a task that previously took 15 to 30 minutes of manual investigation.
The system, built on Amazon Bedrock and an open-source agent framework, automatically pinpoints the root cause of failures in real time, potentially saving engineering teams substantial operational overhead.
What happened
TReNDS, a research center at Georgia State University, built an AI system using Amazon Bedrock and the open-source Strands Agents SDK that automatically investigates production errors in real time, compressing root-cause analysis from 15 to 30 minutes of manual work into under 60 seconds.
Why it matters
Production outages cost time and money; automating error diagnosis at this scale means engineering teams spend far less time firefighting and more time building. For any organization running critical systems, a 15–30× speedup in pinpointing failure sources is a material operational win.
What to watch
The system is built on Amazon Bedrock (AWS's managed service for large language models) and the open-source Strands Agents SDK, making the approach portable to other teams and institutions.
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Automated root-cause analysis represents a direct answer to one of the highest-friction problems in software operations: when a system fails, engineers must manually trace logs, metrics, and code paths to isolate the failure point—a process that consumes time proportional to system complexity and can stretch to half an hour in large environments. TReNDS's approach using agentic AI (an AI system that can autonomously call tools and reason through multi-step tasks) on Amazon Bedrock shifts that burden from human investigation to real-time algorithmic analysis, collapsing the diagnosis window to under one minute. By layering the work on Amazon Bedrock (a managed foundation-model service) and an open-source agent framework, TReNDS has created a reproducible pattern that other research groups and enterprises can adopt, rather than a closed proprietary tool. The outcome is measured in hard operational time savings, not speculative capability.
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