Why AI Agents Fail Without Specs
The models keep getting better and the results keep disappointing. The bottleneck usually isn't the AI. It's what the AI was told.
The evidence
When GitHub studied more than 2,500 instruction files that developers wrote to steer coding agents, the conclusion it published was blunt: “Most agent files fail because they’re too vague.” Not a model problem, not a tooling problem. The instructions simply didn't say enough about what to build.
Engineering has already responded. Spec-driven development, the practice of treating a well-crafted specification as the primary input that AI turns into code, went from niche idea to mainstream practice, with major tool vendors shipping their own flavor of it. The whole movement is one sentence long: the quality of the spec determines the quality of the build.
Why vagueness is fatal to agents specifically
Human teams are resilient to vague requirements because humans push back. An engineer reads "users should be able to share reports" and asks: which users, share with whom, with what permissions, on which plans?
An agent answers those questions itself, instantly and invisibly. Each answer is plausible in isolation. Compounded across a feature, they produce something that looks right in a demo and is wrong everywhere it matters: the edge cases, the permissions, the states nobody described. Vague requirements used to cost a clarifying conversation. Now they cost a rebuild.
The fix is upstream of the IDE
Most spec-driven development tooling starts too late: it assumes a complete spec already exists and focuses on feeding it to the agent. But completeness is a product problem, not an engineering one. The missing information lives in the heads of the people who understand the user, the market, and the constraints.
Getting it out reliably takes interrogation, not inspiration:
- Ask: be interviewed for context. The questions you didn't think to answer are where agent failures come from, and every open option or "TBD" becomes an explicit decision or an explicit exclusion.
- Show: review what the AI adds. Wisary shows every AI suggestion as a diff you approve, so nothing plausible-but-wrong slips in silently.
- Score: measure the document before it ships to the build. Wisary's Insights links every unresolved gap to its exact section, so "looks done" and "is ready" stop being the same claim.
- Ship: export the finished spec as clean, structured Markdown that drops straight into the tools where AI builds.
Agents didn't raise the bar for specs. They removed the safety net that made low-bar specs survivable. Teams that fix the spec fix the agent.