Find the Riskiest Assumption in Your Agent Design
Maurya argues that every plan has a critical flaw hiding in plain sight, and the founder's first job is to find it before burning resources. For an AI-native product, that flaw is rarely the model — it is the assumption that your agent's task is well-defined enough to be trusted. Before you automate anything, name the single assumption that, if wrong, collapses the workflow entirely. Reliable agentic work comes from clear tasks, not from vague autonomy, and clarity only comes from talking to the people the agent is supposed to serve.
Interview for the Workflow Before You Build It
Running Lean's problem interview is not a sales call — it is a structured attempt to invalidate your own hypothesis. Applied to agents, this means getting in front of real users before designing the tool surface, the context window, or the eval harness. A workflow is a fixed path; if you have not confirmed the path is real, you are not building a workflow, you are building a costume. Maurya's cadence — recruit, interview, iterate — is exactly the discipline needed to avoid shipping an agent that solves a problem nobody actually has.
Build the Eval When You Build the Canvas
Maurya treats the lean canvas as a living document, not a one-time artifact — you return to it as each assumption gets tested. The engineering equivalent for an AI-native company is the evaluation harness. Build the eval before you build the agent; let the red cases you discover in early interviews drive the design. A founder who skips this step has no instrument to tell whether a model change made things better or worse, which means they cannot learn, which means Maurya's entire loop — build, measure, learn — breaks at the measurement step.
