Building AI agents without detailed specifications sounds efficient but masks real costs that emerge later in development cycles.
The common argument treats specs as legacy overhead. Developers write a loose prompt describing a goal, let the model explore possible solutions, then iterate on failures. This approach feels faster initially because implementation starts immediately without planning meetings or documentation.
The hidden cost reveals itself in downstream problems. Vague specifications force models to make implicit assumptions about requirements, edge cases, and success criteria. When an agent fails, teams lack clear baselines to diagnose why. Was the failure a model limitation, a misunderstood requirement, or an unspecified constraint? Without specs, debugging becomes expensive guesswork.
Detailed specifications actually compress development time by establishing explicit contracts between intent and implementation. They force teams to think through failure modes upfront. A spec answers critical questions: What should the agent do when it encounters ambiguity? What are hard constraints versus preferences? How should it handle partial information? These decisions matter before coding starts.
The efficiency argument works for throwaway prototypes. For production systems handling real workflows, specs reduce risk and rework. They enable meaningful testing because you know what success looks like. They help new team members understand design decisions. They create audit trails for regulated industries.
The right approach balances rigor with pace. Minimal viable specifications differ from waterfall documentation, but they're not nonexistent. Define the agent's scope, constraints, and failure modes. Specify what data it needs and what outputs matter. Document assumptions about external systems it interacts with.
The O'Reilly piece warns against conflating "agile" with "no planning." Agents operating autonomously in complex environments need enough specification to prevent expensive failures. A rough goal might launch faster, but detailed specs help agents succeed. The choice isn't between specs or speed. It's whether the cost gets paid upfront through planning or downstream through iteration