As companies evolve from simple conversational bots, executive management needs to develop new ways to assess, evaluate, and audit autonomous digital workers Software development traditionally has rudimentary metrics – does the compiled code throw errors or not. Early chatbot development focused on qualitative measures of responses fluency or textual similarity. But when it comes to AI agents that can perform business
Modern cloud infrastructure has become so complicated that it can overwhelm even the most experienced developers. Microservices, multiple cloud hosting platforms, serverless functions, and orchestration frameworks like Kubernetes create trillions of telemetry events every day. When production incidents occur, Site Reliability Engineering and DevOps teams have to wade through tens of thousands of log messages across different monitoring platforms to
For the early years of corporate adoption, business software primarily communicated with the outside world through text. Early language models read text documents, wrote textual summaries, and responded to typed queries. While text-based processing created significant productivity gains for administrative office work, enterprises have significant operations that exist in the physical world where the working data consists of video feeds,
