Platform teams operate differently from product-focused groups. They lack a product manager, revenue targets, or external market pressures. Work only materializes when engineers deliberately create it. The core responsibility of a staff engineer in such teams involves determining what to build next, which the author refers to as "inventing work."

Signals from multiple sources guide this process. System-level indicators include crash reports and postmortems. While these often highlight fixes rather than new opportunities, patterns in failure data can reveal overlooked needs. Cost analysis also provides direction, extending beyond immediate cloud expenses to evaluate broader business unit economics and potential outsourcing decisions.

User behavior offers additional insight. Continuous engagement with users helps identify pain points through structured conversations about recent tasks, frustrations, and unmet needs. Overloaded use-cases—situations where users adapt the platform for unintended purposes—serve as organic prototypes that may warrant integration. Deliberate partner-to-prototype collaborations allow teams to explore such possibilities with users, focusing on whether similar problems exist across the user base.

Organizational signals include OKRs and recurring managerial concerns. Migration challenges also provide clues; teams that struggle to adopt new platform features often reveal gaps in functionality or usability. Industry trends, such as shifts in computing architecture (e.g., from monolithic to modular systems), offer forward-looking guidance. Lagging behind these trends can create opportunities to adopt proven innovations without repeating early-stage experimentation.

The effectiveness of these signals varies. Some, like cost data or OKRs, are readily actionable but may reflect past decisions. Others, such as industry analysis, require more interpretation but can yield stronger strategic insights. The author emphasizes that platform teams should prioritize signals that are both leading and well-supported, avoiding overreliance on lagging indicators or unvalidated managerial input.

Ultimately, the challenge is not a lack of potential work but the risk of building based on the most vocal rather than the most meaningful signals. Recognizing which signals to pursue—and why—remains central to effective platform engineering.