Deployment frequency has become one of the clearest markers of a mature engineering organization. Teams that once shipped monthly now ship daily, and teams that shipped daily now ship several times a day. This shift has largely delivered on its promise. Smaller changes are easier to reason about, rollbacks are faster, and feedback loops are shorter. What has received less attention is the effect this same shift has had on the practice of debugging performance issues. Several assumptions that previously held, a stable release to compare against, a known change set behind a given incident, sufficient time between deployments to observe a system before the next one lands, no longer apply in the same way. A Moving Baseline Performance debugging has traditionally depended on comparison. An engineer examines current behavior against a known good state, typically the previous release, and narrows down what changed. This approach works well when releases are infrequent enough that the previ...
Amazon Web Services (AWS) this week revealed it has added a harness to the open source software development kit (SDK) it makes available for building artificial intelligence (AI) agents. First introduced last year, the Strands SDK makes it simpler for application developers to use AI models to build and deploy AI agents. The Strands harness provides a fully assembled, customizable AI agent that developers can use as a foundation for building agents designed to automate specific tasks. Marc Brooker, vice president and a distinguished engineer at AWS, said the overall goal is to make it simpler for application developers to build an AI agent that has been trained to automate a task that is unique to their organization. In many cases, those AI agents are now being built and deployed by DevOps teams much like any other workload, he noted. The challenge is that bringing together the right set of primitives required to build a custom AI agent can be difficult. The Strands harness provide...