Outcome Over Output Applies to Agents Too
Cagan's central complaint about bad product teams is that they measure output — features shipped, tickets closed — rather than outcomes that matter to customers. Agentic systems fail the same way. An agent that completes tasks reliably against the wrong definition of done is not a success; it is a fast path to confident wrongness. Before wiring up tools and context, a founder must specify what a good result actually looks like, which is exactly the discipline of building an evaluation harness before building the agent itself.
Continuous Discovery Means Continuous Evals
Inspired insists that discovery and delivery run in parallel, never sequentially. You do not finish learning and then build. For agent-native products, evals are the discovery apparatus. Red cases — the inputs where the agent breaks or drifts — reveal what the task definition missed, what context was insufficient, and where tool scope was too loose. Treating evals as a launch checkbox rather than a running loop is the agent equivalent of doing all your user research before writing a single line of code.
Feasibility Lives at the Tool Surface
Cagan gives engineers a specific role in discovery: assessing feasibility before commitment, not after. In agentic work, feasibility lives in tool design. A tool named vaguely, scoped too broadly, or returning unstructured results makes the agent's job harder in ways that no amount of prompt refinement fixes. Tightly scoped tools with structured returns are the engineering constraint that makes outcomes achievable — the hands that determine what the agent can actually do in the world.
Human Judgment Holds the Viable Boundary
Inspired's fourth criterion — viability — asks whether a solution works for the business, not just the customer. Agents cannot own that question. They can optimize within a space, but they cannot define the boundary of acceptable tradeoffs, flag when a workflow is drifting into liability, or decide when a result is good enough to ship. Founders must design that human checkpoint deliberately, not as an afterthought, because an autonomous system without a viable boundary is not empowered — it is unsupervised.
