We built AgentPM because agentic development got fast before it got understandable.

Stephen Sorkin is a technology executive, inventor, and entrepreneur focused on making complex software systems understandable to humans. As co-founder of AgentPM, he is building the evidence layer for AI-assisted software development, helping teams understand what coding agents are doing, why decisions are being made, and how software evolves over time.

Previously, Stephen served as Chief Strategy Officer and SVP of Engineering at Splunk, where he helped build and scale the company's enterprise platform from its earliest years through IPO and beyond. His work spanned distributed systems, security, data analytics, and observability, with a particular focus on turning opaque systems into ones that can be understood, trusted, and controlled.

Stephen holds 148 granted U.S. patents and conducted doctoral research at UC Berkeley under renowned computer scientist John Canny. His current work centers on agentic systems, software intelligence, and human-AI collaboration.

Will Hayes is a technology operator and entrepreneur with more than two decades of experience building and scaling enterprise software companies. He joined Splunk during its earliest days and spent nearly 10 years helping build the company from its first alpha release through IPO. During that time, he built and led the technology ecosystem and partnerships organization, helping establish Splunk as a leading data platform and category-defining enterprise software company.

He later served as CEO of Lucidworks for nine years, leading the company's transformation from an open-source services business into a commercial AI software platform serving some of the world's largest retailers and digital commerce brands.

Today, as co-founder of AgentPM, Will is focused on helping organizations understand, adopt, and scale AI-assisted software development. His experience building platforms, ecosystems, and enterprise software businesses informs AgentPM's mission to become the operating layer for the age of coding agents.