Chain of Thought The AI Agent Trust Gap: Bridging Risk to Reliability | Elastic’s Philipp Krenn
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Jul 16, 2025 Philipp Krenn, Director of Developer Relations at Elastic, discusses the critical need for trust and reliability in AI agents. He highlights the new AI reliability platform from Galileo and its role in enhancing developer experiences. The conversation touches on Elastic's evolution towards AI innovations, including Retrieval-Augmented Generation and specialized language models. Krenn emphasizes the importance of robust testing and guardrails for building high-performing AI systems, ensuring they meet the demands of modern enterprises.
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Complexity of Agent Reliability
- Agent reliability is vastly more complex than simple prompt engineering or chat completions. - It requires scalable, trustworthy systems to manage multi-task AI agents in enterprise environments.
Automate Failure Insights Early
- Use automated reasoning engines to surface failure modes early and guide developers to fixes. - This reduces debugging time and paves way for self-healing agent systems.
Value of Small Language Models
- Small language models (SLMs) enable real-time evaluation and guardrails at lower cost and latency compared to large LLMs. - Specially fine-tuned SLMs like Luna 2 outperform generic open-source models in efficiency and adaptability.