Generative AI agents in production environments demand resilience strategies that go beyond traditional software patterns. AI agents make autonomous decisions, consume substantial computational resources, and interact with external systems in unpredictable ways. These characteristics create failure modes that conventional resilience approaches might not address. This post presents a framework for AI agent resilience risk analysis […]| Amazon Web Services
As organizations rapidly deploy large language models (LLMs) and generative AI agents to power increasingly intelligent workloads, they struggle to monitor and troubleshoot the complex interactions within their AI applications. Traditional monitoring tools fall short in providing the visibility across components, leading to developers and AI/ML engineers to manually correlate interaction logs or building custom […]| Amazon Web Services
Amazon Bedrock| Amazon Web Services, Inc.
In my previous blog, I shared how to evolve leadership for agentic AI using familiar mental models. As a CTO, I’ve been thinking about the corresponding architectural shifts required: We need to move from building predictable systems to developing autonomous capabilities that augment teams. Based on hands-on explorations and working with fellow technology leaders navigating […]| Amazon Web Services