What Is Self-Driving Production? The Future of AI SRE Explained

Envision this: A ServiceNow ticket arrives at a big bank, and 50 customers can't log into their accounts. All the dashboards show a red indicator. About 60 to 100 people end up in a war-room meeting and remain there for about two hours. They discover the problem was a slow SQL query in a database ten hops down the stack, a system neither the login team nor their monitoring system had originally identified. That is the situation which Anish Agarwal, CEO of Traversal, alludes to when explaining why incident response is becoming more difficult and why he believes the solution must be designed differently from the observability tools that most enterprises currently operate. In the recent episode of the **Tech Transformed** podcast, Dan Twing, President and COO of Enterprise Management Associates (EMA), sat down with Agarwal, also a professor at Columbia University. They discuss how code laid out by artificial intelligence (AI) is affecting production incidents. They also talk about what is needed to trace symptoms back to their causes within a portfolio of 5,000 applications, and how businesses decide when to switch from using AI suggestions to adopting autonomous remediation. In terms of reliability, Agarwal says that the enterprise has to question whether the task was not just initiated but completed. However, it's also possible for a job to finish, yet the answer might not be the one you want. ### Key Takeaways - "Did it run?" no longer proves an AI-built system worked. - Coding agents are trained on compile-and-run, so they can sideline the real goal. - Same configuration, three runs, three different results. - Engineers now write specs; agents write code, so code comprehension drops. - Testing improves, but failures still happen at the seams between systems. - Agents need to search far more data than dashboards were built for. - Traversal's production world model maps entities, connections and live telemetry. - Customers are using it to validate CMDBs, which was unplanned. - Causal analysis separates upstream and downstream anomalies from spurious ones. - Multiple paths converging on one cause builds confidence in a root cause. - L4 cut a war room from 60–100 people to five to 10. - Reported MTTR fell from two or three hours to 20–30 minutes. - Coding agents now call Traversal's MCP to write production-aware code. - Decide up front when to build in-house versus buy. ### Chapters - 00:00 Introduction to Hybrid Cloud Challenges - 02:53 The Role of AI in Production Systems - 06:34 Complexity of Troubleshooting in AI Systems - 12:03 Causal Analysis in Production Environments - 18:03 Agentic Systems and Human Collaboration - 22:56 Framework for Agentic Readiness in Reliability - 27:03 Future of Self-Driving Production - 29:55 Practical Steps for Tech Leaders Visit traversal.com. agentic SRE, self-driving production, site reliability engineering, AI SRE, AI incident response, root cause analysis, causal AI, MTTR, mean time to resolution, production world model, CMDB, CMDB validation, ServiceNow, observability, AIOps, incident management, Traversal, Anish Agarwal, Dan Twing, Tech Transformed, EM360Tech, Enterprise Management 360, AI-generated code, coding agents, DevOps, enterprise IT