Show Notes
AI agents have become capable of reasoning across large amounts of data, calling tools, and taking sequences of actions autonomously. These qualities make them well suited to some of the most persistent pain points in DevOps, including the on-call engineer woken at 3 AM to diagnose an incident, the build failure that takes hours to trace back to a root cause, and the operational toil of modern software delivery. Agentic DevOps is the emerging paradigm that applies these capabilities to the full software lifecycle, with the goal of matching the velocity of modern software delivery with an equally capable operational layer.
Neha Gaswamy leads Agentic DevOps at AWS and has been at Amazon for over twenty years. In this episode, she joins Matt Merrill to discuss the AWS approach to agentic DevOps, how Amazon dogfoods its own DevOps tooling, how their DevOps agent works from alarm to root cause, why determinism still matters in an agentic world, creative MCP integrations customers are building, and what the future holds for SRE engineers as agents take on more of the operational work.
Sponsorship inquiries: [email protected]
The post Agentic DevOps at AWS appeared first on Software Engineering Daily.
More from Software Engineering Daily
Codebases in enterprise organizations often span hundreds of thousands, or even millions, of lines of code. In many cases, changes must follow established patterns precisely, or risk introducing costly security or operational issues. A common challenge with AI coding tools is that they struggle at this scale: LLM context windows cover only a fraction of a large codebase, and output quality degrades the longer an agent runs.
In recent years, the main impact of AI has been on the digital world, and most notably software engineering. However, its longer-term effect on the physical world may prove even more profound, reshaping how cars drive themselves, how factories run, and how robots move through homes and warehouses.
Software testing is widely understood as essential to building reliable systems, and it’s one of the hardest things to get right. A fundamental issue is that tests are good at confirming the behavior a team expects, but the most damaging failures tend to hide in the edge cases no one thought to check during development. Those blind spots are only growing as AI produces more code that no human has closely reviewed.
SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer break down the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry.
The popular story of Steve Jobs is that he co-founded Apple, was pushed out, wandered through a failed venture called NeXT, then returned to save the company and usher in the iPod and iPhone. That version is memorable, widely repeated, and, in an important sense, wrong. His years at NeXT are usually treated as a footnote, when they were among the most consequential of his life and career.