Episode
Revolutionizing Production Systems: The Resolve AI Approach
- Podcast
- AI Engineering Podcast
- Published
- Sep 4, 2025
- Duration seconds
- 3061
- Processing state
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Summary
Summary In this episode of the AI Engineering Podcast, CEO of Resolve AI Spiros Xanthos shares his insights on building agentic capabilities for operational systems. He discusses the limitations of traditional observability tools and the need for AI agents that can reason through complex systems to provide actionable insights and solutions. The conversation highlights the architecture of Resolve AI, which integrates with existing tools to build a comprehensive understanding of production environments, and emphasizes the importance of context and memory in AI systems. Spiros also touches on the evolving role of AI in production systems, the potential for AI to augment human operators, and the need for continuous learning and adaptation to fully leverage these advancements. Announcements Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems Your host is Tobias Macey and today I'm interviewing Spiros Xanthos about architecting agentic capabilities for operational challenges with managing production systems. Interview Introduction How did you get involved in machine learning? Can you describe what Resolve AI is and the story behind it? We have decades of experience as an industry in managing operational complexity. What are the critical failures in capabilities that you are addressing with the application of AI? Given the existing capabilities of dedicated platforms (e.g. Grafana, PagerDuty, Splunk, etc), what is your reasoning for building a new system vs. a new feature of existing operational product? Over the past couple of years the industry has developed a growing number of agent patterns. What was your approach in evaluating and selecting a particular approach for your product? One of the…