Proactive failure testing has increased confidence and reliability across complex microservicesThere are certain areas where I think Gremlin Reliability Management Platform can improve. I would certainly add features related to AI and GenAI for recommendations. While dependency identification works well seamlessly, deeper dependency intelligence is lacking. When you have deeper dependencies, reliability management can struggle with identifying those deeper dependency intelligences. Having the intelligence to deeply analyze dependencies will be very helpful. Regarding reliability scores, while those scores are good, having more actionable reliability scores is something I would recommend adding to the existing system. Initially, when I started with Gremlin Reliability Management Platform certification programs, I completed two certifications with Gremlin—one professional and another practitioner. While that certification has helped me, an easier onboarding and learning curve for bringing people into this program can be adjusted. That is one current gap I see. If you really look at the cost-benefit visibility, it is not very evident by using Gremlin Reliability Management Platform. If Gremlin Reliability Management Platform could help realize that and bring in visibility regarding the cost versus the benefit, that is the reason I provide a score less than ten. From a standpoint of simulating complex real-world failures, I believe there is still a gap concerning gap identification. Currently, Gremlin Reliability Management Platform mainly focuses on infrastructure-level failures and does not really simulate business logic failures, data corruption scenarios, or potential failures across regions. One of the key elements needing improvement with Gremlin Reliability Management Platform is the limited team workflow integration I see in my organization. Collaboration and limited team workflow integration are areas I would highlight as needing improvement.
